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Very short-lived substances (VSLS), including dichloromethane (CH2Cl2), chloroform (CHCl3), perchloroethylene (C2Cl4), and 1,2-dichloroethane (C2H4Cl2), are a stratospheric chlorine source and therefore contribute to ozone depletion. We quantify stratospheric chlorine trends from these VSLS (VSLCltot) using a chemical transport model and atmospheric measurements, including novel high-altitude aircraft data from the NASA VIRGAS (2015) and POSIDON (2016) missions. We estimate VSLCltot increased from 69 (±14) parts per trillion (ppt) Cl in 2000 to 111 (±22) ppt Cl in 2017, with >80% delivered to the stratosphere through source gas injection, and the remainder from product gases. The modeled evolution of chlorine source gas injection agrees well with historical aircraft data, which corroborate reported surface CH2Cl2 increases since the mid-2000s. The relative contribution of VSLS to total stratospheric chlorine increased from ~2% in 2000 to ~3.4% in 2017, reflecting both VSLS growth and decreases in long-lived halocarbons. We derive a mean VSLCltot growth rate of 3.8 (±0.3) ppt Cl/year between 2004 and 2017, though year-to-year growth rates are variable and were small or negative in the period 2015-2017. Whether this is a transient effect, or longer-term stabilization, requires monitoring. In the upper stratosphere, the modeled rate of HCl decline (2004-2017) is -5.2% per decade with VSLS included, in good agreement to ACE satellite data (-4.8% per decade), and 15% slower than a model simulation without VSLS. Thus, VSLS have offset a portion of stratospheric chlorine reductions since the mid-2000s.

The recovery and re-calibration of a dataset of vertical aerosol extinction profiles of the 1963/64 stratospheric aerosol layer measured by a searchlight at 32° N in New Mexico, US, is reported. The recovered dataset consists of 105 aerosol extinction profiles at 550 nm that cover the period from December 1963 to December 1964. It is a unique record of the portion of the aerosol cloud from the March 1963 Agung volcanic eruption that was transported into the Northern Hemisphere subtropics. The data-recovery methodology involved re-digitizing the 105 original aerosol extinction profiles from individual Figures within a research report, followed by the re-calibration. It involves inverting the original equation used to compute the aerosol extinction profile to retrieve the corresponding normalized detector response profile. The re-calibration of the original aerosol extinction profiles used Rayleigh extinction profiles calculated from local soundings. Rayleigh and aerosol slant transmission corrections are applied using the MODTRAN code in transmission mode. Also, a best-estimate aerosol phase function was calculated from observations and applied to the entire column. The tropospheric aerosol phase function from an AERONET station in the vicinity of the searchlight location was applied between 2.76 to 11.7 km. The stratospheric phase function, applied for a 12.2 to 35.2 km altitude range, is calculated from particle-size distributions measured by a high-altitude aircraft in the vicinity of the searchlight in early 1964. The original error estimate was updated considering unaccounted errors. Both the re-calibrated aerosol extinction profiles and the re-calibrated stratospheric aerosol optical depth magnitudes showed higher magnitudes than the original aerosol extinction profiles and the original stratospheric aerosol optical depth, respectively. However, the magnitudes of the re-calibrated variables show a reasonable agreement with other contemporary observations. The re-calibrated stratospheric aerosol optical depth demonstrated its consistency with the tropics-to-pole decreasing trend, associated with the major volcanic eruption stratospheric aerosol pattern when compared to the time-coincident stratospheric aerosol optical depth lidar observations at Lexington at 42° N. [ABSTRACT FROM AUTHOR]

A growing number of general circulation models are adapting interactive sulfur and aerosol schemes to improve the representation of relevant physical and chemical processes and associated feedbacks. They are motivated by investigations of climate response to major volcanic eruptions and potential solar geoengineering scenarios. However, uncertainties in these schemes are not well constrained. Stratospheric sulfate is modulated by emissions of sulfur-containing species of anthropogenic and natural origin, including volcanic activity. While the effects of volcanic eruptions have been studied in the framework of global model intercomparisons, the background conditions of the sulfur cycle have not been addressed in such a way. Here, we fill this gap by analyzing the distribution of the main sulfur species in nine global atmospheric aerosol models for a volcanically quiescent period. We use observational data to evaluate model results. Overall, models agree that the three dominant sulfur species in terms of burdens (sulfate aerosol, OCS, and SO 2) make up about 98 % stratospheric sulfur and 95 % tropospheric sulfur. However, models vary considerably in the partitioning between these species. Models agree that anthropogenic emission of SO 2 strongly affects the sulfate aerosol burden in the northern hemispheric troposphere, while its importance is very uncertain in other regions, where emissions are much lower. Sulfate aerosol is the main deposited species in all models, but the values deviate by a factor of 2. Additionally, the partitioning between wet and dry deposition fluxes is highly model dependent. Inter-model variability in the sulfur species is low in the tropics and increases towards the poles. Differences are largest in the dynamically active northern hemispheric extratropical region and could be attributed to the representation of the stratospheric circulation. The differences in the atmospheric sulfur budget among the models arise from the representation of both chemical and dynamical processes, whose interplay complicates the bias attribution. Several problematic points identified for individual models are related to the specifics of the chemistry schemes, model resolution, and representation of cross-tropopause transport in the extratropics. Further model intercomparison research is needed with a focus on the clarification of the reasons for biases, given the importance of this topic for the stratospheric aerosol injection studies. [ABSTRACT FROM AUTHOR]

The January 2022 eruption of Hunga Tonga‐Hunga Ha'apai (HTHH) injected a huge amount (∼150 Tg) of water vapor (H2O) into the stratosphere, along with small amount of SO2. An off‐line 3‐D chemical transport model (CTM) successfully reproduces the spread of the injected H2O through October 2023 as observed by the Microwave Limb Sounder. Dehydration in the 2023 Antarctic polar vortex caused the first substantial (∼20 Tg) removal of HTHH H2O from the stratosphere. The CTM indicates that this process will dominate removal of HTHH H2O for the coming years, giving an overall e‐folding timescale of 4 years; around 25 Tg of the injected H2O is predicted to still remain in the stratosphere by 2030. Following relatively low Antarctic column ozone in midwinter 2023 due to transport effects, additional springtime depletion due to H2O‐related chemistry was small and maximized at the vortex edge (10 DU in column). Plain Language Summary: Around 150 Tg (150 million tons) of water vapor was injected into the stratosphere during the eruption of Hunga Tonga‐Hunga Ha'apai. Water vapor is a greenhouse gas and this increase is expected to have a warming effect in the troposphere, as well causing perturbations in stratospheric chemistry and aerosols. We use an atmospheric model to study the residence time of this excess water vapor and its impact on the recent Antarctic ozone hole. The model performance is evaluated by comparison with satellite measurements. Wintertime dehydration in the Antarctic stratosphere in 2023 is found to be an important mechanism for removal of the volcanic water from the stratosphere. However, the overall removal rate is predicted to be slow; around 25 Tg (17%) is still present in 2030. The direct impact of the excess water vapor on ozone via chemical processes in the Antarctic ozone hole in 2023 is small. Key Points: Antarctic dehydration is a major removal pathway of stratospheric H2O injected from Hunga Tonga‐Hunga Ha'apai (HTHH) eruptionHTHH H2O caused small (up to 10 DU) additional chemical ozone depletion in 2023 Antarctic springModel indicates e‐folding timescale of 4 years for removal of HTHH H2O from stratosphere [ABSTRACT FROM AUTHOR]

Using state-of-the-art satellite ozone profile products, and a chemical transport model, we provide an updated estimate of the tropospheric ozone radiative effect (TO 3 RE) 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 TO 3 RE to be 1.17 ± 0.03 W m -2. However, from our analysis, using decadal (2008–2017) ozone profile datasets from the Infrared Atmospheric Sounding Interferometer, average TO 3 RE ranges between 1.21 and 1.26 W m -2. Over this decade, the modelled and observational TO 3 RE linear trends show a negligible change (e.g. ± 0.1 % yr -1). Two model sensitivity experiments fixing emissions and meteorology to 1 year (i.e. start year – 2008) show that temporal changes in ozone precursor emissions (increasing contribution) and meteorological factors (decreasing contribution) have counteracting tendencies, leading to a negligible globally weighted average TO 3 RE trend. [ABSTRACT FROM AUTHOR]

Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon seasons, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx=NO+NO2). We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO 2) data, from S5P aboard TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emissions sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre-monsoon (February–May) and post-monsoon (October–February) seasons. According to this analysis, during the pre-monsoon season, the highest accumulated satellite TCNO 2 trajectories come from the east and north-west of Delhi. TCNO 2 is elevated within Delhi and the Indo-Gangetic Plain (IGP) to the east of city. The accumulated NOx emission trajectories indicate that the transport and industry sectors together account for more than 80 % of the total accumulated emissions, which are dominated by local sources (>70 %) under easterly winds and north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this season). During the post-monsoon season the highest accumulated satellite TCNO 2 trajectories are advected from Punjab and Haryana, where satellite TCNO 2 is elevated, indicating the potential for the long-range transport of agricultural burning emissions to Delhi. However, accumulated NOx emissions indicate local (70 %) emissions from the transport sector are the largest contributor to the total accumulated emissions. High local emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary layer, aid the build-up of emissions locally and along the trajectory path. This indicates the possibility that fire emissions datasets may not capture emissions from agricultural waste burning in the north-west sufficiently to accurately quantify their influence on Delhi air quality (AQ). Analysis of daily ground-based NO2 observations indicates that high-pollution episodes (>90 th percentile) occur predominantly in the post-monsoon season, and more than 75 % of high-pollution events are primarily caused by local sources. But there is also a considerable influence from non-local (30 %) emissions from the transport sector during the post-monsoon season. Overall, we find that in the post-monsoon season, there is substantial accumulation of high local NOx emissions from the transport sector (70 % of total emissions, 70 % local), alongside the import of NOx pollution into Delhi (30 % non-local). This work indicates that both high local NOx emissions from the transport sector and the advection of highly polluted air originating from outside Delhi are of concern for the population. As a result, air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport and agricultural waste burning sectors, particularly during the post-monsoon season. [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]

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-for41 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]

Ozone is a potent air pollutant in the lower troposphere and an important short-lived climate forcer (SLCF) in the upper troposphere. Studies using satellite data to investigate spatiotemporal variability of troposphere ozone (TO 3) have predominantly focussed on the tropospheric column metric. This is the first study to investigate long-term spatiotemporal variability in lower tropospheric column ozone (LTCO 3 , surface–450 hPa sub-column) by merging multiple European Space Agency–Climate Change Initiative (ESA-CCI) products produced by the Rutherford Appleton Laboratory (RAL) Space. We find that in the LTCO 3 , the degree of freedom of signal (DOFS) from these products varies with latitude range and season and is up to 0.8, indicating that the retrievals contain useful information on lower TO 3. The spatial and seasonal variation of the RAL Space products are in good agreement with each other, but there are systematic offsets of up to 3.0–5.0 DU between them. Comparison with ozonesondes shows that the Global Ozone Monitoring Experiment (GOME-1, 1996–2003), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2010) and the Ozone Monitoring Instrument (OMI, 2005–2017) have stable LTCO 3 records over their respective periods, which can be merged together. However, GOME-2 (2008–2018) shows substantial drift in its bias with respect to ozonesondes. We have therefore constructed a robust merged data set of LTCO 3 from GOME-1, SCIAMACHY and OMI between 1996 and 2017. Comparing the LTCO 3 differences between the 1996–2000 and 2013–2017 5-year averages, we find sizeable positive increases (3.0–5.0 DU) in the tropics/sub-tropics, while in the northern mid-latitudes, we find small-scale differences in LTCO 3. Therefore, we conclude that there has been a substantial increase in tropical/sub-tropical LTCO 3 during the satellite era, which is consistent with tropospheric column ozone (TCO 3) records from overlapping time periods (e.g. 2005–2016). [ABSTRACT FROM AUTHOR]

Monitoring the atmospheric concentrations of greenhouse gases (GHGs) is crucial to improve our understanding of their climate impact. However, there are no long-term profile data sets of important GHGs that can be used to gain a better insight into the processes controlling their variations in the atmosphere. In this study, we apply corrections to chemical transport model (CTM) output based on profile measurements from two solar occultation instruments: the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS). The goal is to construct long-term (1991–2021), gap-free stratospheric profile data sets, hereafter referred to as TCOM, for two important GHGs. To estimate the corrections that need to be applied to the CTM profiles, we use the extreme gradient boosting (XGBoost) regression model. For methane (TCOM-CH4), we utilize both HALOE and ACE satellite profile measurements from 1992 to 2018 to train the XGBoost model, while profiles from 2019 to 2021 serve as an independent evaluation data set. As there are no nitrous oxide (N 2 O) profile measurements for earlier years, we derive XGBoost-derived correction terms to construct TCOM-N2O profiles using only ACE-FTS profiles from the 2004–2018 time period, with profiles from 2019–2021 used for the independent evaluation. Overall, both TCOM-CH4 and TCOM-N2O profiles exhibit excellent agreement with the available satellite-measurement-based data sets. We find that compared to evaluation profiles, biases in TCOM-CH4 and TCOM-N2O are generally less than 10 % and 50 %, respectively, throughout the stratosphere. The daily zonal mean profile data sets, covering altitude (15–60 km) and pressure (300–0.1 hPa) levels, are publicly available via the following links: 10.5281/zenodo.7293740 for TCOM-CH4 and 10.5281/zenodo.7386001 for TCOM-N2O. [ABSTRACT FROM AUTHOR]

Substantial and prolonged enhancements in stratospheric water vapor (SWV) have occurred after large‐magnitude explosive tropical volcanic eruptions, with modified tropopause entry caused by aerosol‐absorptive heating. Here, we analyze the timing and longevity of heating‐driven post‐eruption SWV changes within CMIP6‐VolMIP short‐term climate‐response experiments with the UK Earth System Model (UKESM1). We find aerosol‐absorptive heating causes peak SWV increases of 17% (∼1 ppmv) and 10% (0.5 ppmv) at 100 and 50 hPa, at ∼18 and ∼23 months after a Pinatubo‐like eruption, respectively. We track the temperature response in the tropical lower stratosphere and identify the main SWV increase occurs only after the descending aerosol heating reaches the tropopause, suggesting a key role for aerosol microphysical processes (sedimentation rate). We explore how El Niño–Southern Oscillation variability modulates this effect. Post‐eruption SWV increases are ∼80% stronger for the La Nina phase compared to the ensemble mean. Tropical upwelling strongly mediates this effect. Plain Language Summary: Strong volcanic eruptions, such as the 1991 eruption of Mt Pinatubo, inject a large amount of SO2 directly into the stratosphere, thereby enhancing the stratospheric aerosol layer and causing a short‐term climatic perturbation. Another substantial part of the climatic influence is the change in stratospheric water vapor (SWV), which affects the chemical processes and the radiative budget of the atmosphere. Along with near‐instantaneous injection of water vapor into the stratosphere, volcanic eruptions can indirectly enhance the entry of water vapor into the stratosphere through aerosol‐induced tropopause heating. This work analyses Earth system model experiments designed to explore how volcanic impacts combine with internal climate variability. We find that peak SWV entry mixing ratios occur only within the second post‐eruption year, consistent with the substantially lagged timing of SWV increase seen in post‐Pinatubo satellite measurements. This analysis provides a new perspective on the temporal evolution of the observed post‐Pinatubo SWV increase and an improved quantification of its impacts. Key Points: Aerosol‐induced absorptive‐heating increases stratospheric water vapor (SWV) by up to 17% at 18 months post‐eruption in a Pinatubo‐like experimentAnalyzing simulations by El Niño–Southern Oscillation (ENSO) variability show an 80% larger peak SWV increase occurs if an eruption is followed by a La Niña phaseThe timing of peak SWV increase occurs when volcanic‐aerosol‐induced heating reaches the tropopause, with ENSO modulation of tropical upwelling [ABSTRACT FROM AUTHOR]

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, and non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis; however, the complex coupling in many atmospheric processes can make it prone to the issue of over-fitting when using the conventional ordinary-least-squares (OLS) approach. To overcome this issue, here we adopt a regularized (ridge) regression method to estimate ozone trends and quantify the influence of individual processes. We use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged dataset (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Besides SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT) and output from a chemical transport model (TOMCAT) simulation forced with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. For 1984–1997, we observe smaller negative trends in the SWOOSH stratospheric ozone profile using ridge regression compared to OLS. Except for the tropical lower stratosphere, the largest differences arise in the mid-latitude lowermost stratosphere (>4 % per decade difference at 100 hPa). From 1998 and the onset of ozone recovery in the upper stratosphere, the positive trends estimated using the ridge regression model (∼1 % per decade near 2 hPa) are smaller than those using OLS (∼2 % per decade). In the lower stratosphere, post-1998 negative trends with large uncertainties are observed and ridge-based trend estimates are somewhat smaller and less variable in magnitude compared to the OLS regression. Aside from the tropical lower stratosphere, the largest difference is around 2 % per decade at 100 hPa (with ∼3 % per decade uncertainties for individual trends) in northern mid-latitudes. For both time periods the SWOOSH data produce large negative trends in the tropical lower stratosphere with a correspondingly large difference between the two trend methods. In both cases the ridge method produces a smaller trend. The regression coefficients from both OLS and ridge models, which represent ozone variations associated with natural processes (e.g. the quasi-biennial oscillation, solar variability, El Niño–Southern Oscillation, Arctic Oscillation, Antarctic Oscillation, and Eliassen–Palm flux), highlight the dominance of dynamical processes in controlling lower-stratospheric ozone concentrations. Ridge regression generally yields smaller regression coefficients due to correlated explanatory variables, and care must be exercised when comparing fit coefficients and their statistical significance across different regression methods. Comparing the ML-TOMCAT-based trend estimates with the ERA5-forced model simulation, we find ML-TOMCAT shows significant improvements with much better consistency with the SWOOSH dataset, despite the ML-TOMCAT training period overlapping with SWOOSH only for the Microwave Limb Sounder (MLS) measurement period. The largest inconsistencies with respect to SWOOSH-based trends post-1998 appear in the lower stratosphere where the ERA5-forced model simulation shows positive trends for both the tropics and the mid-latitudes. The large differences between satellite-based data and the ERA5-forced model simulation confirm significant uncertainties in ozone trend estimates, especially in the lower stratosphere, underscoring the need for caution when interpreting results obtained with different regression methods and datasets. [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]

Sulfate aerosol in the stratosphere is an important climate driver, causing solar dimming in the years after major volcanic eruptions. Hence, a growing number of general circulation models are adapting interactive sulfur and aerosol schemes to improve the representation of relevant chemical processes and associated feedbacks. However, uncertainties of these schemes are not well constrained. Stratospheric sulfate is modulated by natural emissions of sulfur-containing species, including volcanic eruptive, and anthropogenic emissions. Model intercomparisons have examined the effects of volcanic eruptions, whereas the background conditions of the sulfur cycle have not been addressed in a global model intercomparison project. Assessing background conditions in global models allows us to identify model discrepancies as they are masked by large perturbations such as volcanic eruptions, yet may still matter in the aftermath of such a disturbance. Here, we analyze the atmospheric burden, seasonal cycle, and vertical and meridional distribution of the main sulfur species among nine global atmospheric aerosol models that are widely used in the stratospheric aerosol research community. We use observational and reanalysis data to evaluate model results. Overall, models agree that the three dominant sulfur species in terms of burdens (sulfate aerosol, OCS, and SO2) make up about 98 % of stratospheric sulfur and 95 % of tropospheric sulfur. However, models vary considerably in the partitioning between these species. Models agree that anthropogenic emission of SO2 strongly affects the sulfate aerosol burden in the Northern Hemispheric troposphere, while its importance is very uncertain in other regions. The total deposition of sulfur varies among models, deviating by a factor of two, but models agree that sulfate aerosol is the main form in which sulfur is deposited. Additionally, the partitioning between wet and dry deposition fluxes is highly model dependent. We investigate the areas of greatest variability in the sulfur species burdens and find that inter-model variability is low in the tropics and increases towards the poles. Seasonality in the southern hemisphere is depicted very similar among models. Differences are largest in the dynamically active northern hemispheric extratropical region, hence some of the differences could be attributed to the differences in the representation of the stratospheric circulation among underlying general circulation models. This study highlights that the differences in the atmospheric sulfur budget among the models arise from the representation of both chemical and dynamical processes, whose interplay complicates the bias attribution. Several problematic points identified for individual models are related to the specifics of the chemistry schemes, model resolution, and representation of cross-tropopause transport in the extratropics. Further model intercomparison research is needed focusing on the clarification of the reasons for biases, given also the importance of this topic for the stratospheric aerosol injection studies. [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 using satellite data to investigate spatiotemporal variability of troposphere ozone (TO3) have predominantly focussed on the tropospheric column metric. This is the first study to investigate long-term spatiotemporal variability in lower tropospheric column ozone (LTCO3, surface-450 hPa sub-column) by merging multiple European Space Agency – Climate Change Initiative (ESA-CCI) products produced by the Rutherford Appleton Laboratory (RAL) Space. We find that in the LTCO3, the degrees of freedom of signal (DOFS) from these products varies with latitude range and season and is up to 0.65, indicating that the retrievals contain useful information on lower TO3. The spatial and seasonal variation of the RAL Space products are in good agreement with each other but there are systematic offsets of up to 3.0–5.0 DU between them. Comparison with ozonesondes shows that the Global Ozone Monitoring Experiment (GOME-1, 1996–2003), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2010) and the Ozone Monitoring Instrument (OMI, 2005–2017) have stable LTCO3 records over their respective periods, which can be merged together. While GOME-2 (2008–2018) shows substantial drift in its bias with respect to ozonesondes. We have therefore constructed a robust merged dataset of LTCO3 from GOME-1, SCIAMACHY and OMI between 1996 and 2017. Comparing the LTCO3 differences between the 1996–2000 and 2013–2017 5-year averages, we find significant positive increases (3.0–5.0 DU) in the tropics/sub-tropics, while in the northern mid-latitudes, we find small scale differences in LTCO3. Therefore, we conclude that there has been a substantial increase in tropical/sub-tropical LTCO3 during the satellite-era. [ABSTRACT FROM AUTHOR]

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis, however, the complex coupling in most atmospheric processes can make it prone to the over-fitting or multi-collinearity-related issues when using the conventional Ordinary Least Squares (OLS) setting. To overcome this issue, we adopt a regularised (Ridge) regression method to estimate ozone trends and quantify the influence of individual processes. Here, we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged data set (v2.7) to derive stratospheric ozone profile trends for the period 1984-2020. Beside SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT), and output from two chemical transport model (TOMCAT) simulations forced with ECMWF reanalyses ERA-Interim and ERA5. With Ridge regression, the stratospheric ozone profile trends from SWOOSH data show smaller declines during 1984-1997 compared to OLS with the largest differences in the lowermost stratosphere (>4 % per decade at 100 hPa). Upper stratospheric ozone has increased since 1998 with maximum (~2 % per decade near 2 hPa) in local winter for mid-latitudes. Negative trends with large uncertainties are observed in the lower stratosphere with the most pronounced in the tropics. The largest differences in post-1998 trend estimates between OLS and Ridge regression methods appear in the tropical lower stratosphere (with ~7 % per decade difference at 100 hPa). Ozone variations associated with natural processes such as the quasi-biennial oscillation QBO), the solar variability, the El Niño-Southern Oscillation (ENSO), the Arctic oscillation (AO) and the Antarctic oscillation (AAO) also indicate that Ridge regression coefficients are somewhat smaller and less variable compared to the OLS-based estimates. Additionally, ML-TOMCAT based trend estimates are consistent with SWOOSH data set. Finally, we argue that the large differences between the satellite-based data and model simulations confirm that there are still large uncertainties in ozone trend estimates especially in the lower stratosphere, and caution is needed when discussing results if explanatory variables used are correlated. [ABSTRACT FROM AUTHOR]

Accurate quantification of long-term trends in stratospheric ozone can be challenging due to their sensitivity to natural variability, the quality of the observational datasets, non-linear changes in forcing processes as well as the statistical methodologies. Multivariate linear regression (MLR) is the most commonly used tool for ozone trend analysis, however, the complex coupling in most atmospheric processes can make it prone to the over-fitting or multi-collinearity-related issues when using the conventional Ordinary Least Squares (OLS) setting. To overcome this issue, we adopt a regularised (Ridge) regression method to estimate ozone trends and quantify the influence of individual processes. Here, we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) merged data set (v2.7) to derive stratospheric ozone profile trends for the period 1984–2020. Beside SWOOSH, we also analyse a machine-learning-based satellite-corrected gap-free global stratospheric ozone profile dataset from a chemical transport model (ML-TOMCAT), and output from two chemical transport model (TOMCAT) simulations forced with ECMWF reanalyses ERA-Interim and ERA5. With Ridge regression, the stratospheric ozone profile trends from SWOOSH data show smaller declines during 1984–1997 compared to OLS with the largest differences in the lowermost stratosphere (> 4 % per decade at 100 hPa). Upper stratospheric ozone has increased since 1998 with maximum (~2 % per decade near 2 hPa) in local winter for mid-latitudes. Negative trends with large uncertainties are observed in the lower stratosphere with the most pronounced in the tropics. The largest differences in post-1998 trend estimates between OLS and Ridge regression methods appear in the tropical lower stratosphere (with ~7 % per decade difference at 100 hPa). Ozone variations associated with natural processes such as the quasi-biennial oscillation (QBO), the solar variability, the El Niño–Southern Oscillation (ENSO), the Arctic oscillation (AO) and the Antarctic oscillation (AAO) also indicate that Ridge regression coefficients are somewhat smaller and less variable compared to the OLS-based estimates. Additionally, ML-TOMCAT based trend estimates are consistent with SWOOSH data set. Finally, we argue that the large differences between the satellite-based data and model simulations confirm that there are still large uncertainties in ozone trend estimates especially in the lower stratosphere, and caution is needed when discussing results if explanatory variables used are correlated. [ABSTRACT FROM AUTHOR]

Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon months, the prevailing northwesterly winds are known to influence aerosol pollution events in Delhi, by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx =NO + NO2), a hazardous primary air pollutant for health, which can lead to the formation of secondary aerosols and ozone. We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO2) data, from S5P on-board TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emission sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre- (February - March) and post- (October - January) monsoon periods. During the premonsoon period, the residential and transport sectors together account for more than 50% of the total accumulated emissions, which are dominated by local sources (90%) under easterly winds and by non35 local sources (>70%) under north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this period). During the post-monsoon period non-local (60%) transport emissions are the largest contributor to the total accumulated emissions as high emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary-layer aid the build-up of emissions along the trajectory path. Analysis of surface daily NO2 observations indicates that high pollution episodes (> 90th percentile) occur predominantly in the post-monsoon and more than 75% of high pollution events are primarily caused by non-local sources. Overall, we find that in the post-monsoon period, there is a substantial import of NOx pollution into Delhi with a large contribution from the transport sector. This work indicates that the advection of highly polluted air originating from outside Delhi is of concern for the population and air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport sector, particularly during post-monsoon months. [ABSTRACT FROM AUTHOR]

Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon months, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi, by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx = NO + NO2), a hazardous primary air pollutant for health, which can lead to the formation of secondary aerosols and ozone. We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO2) data, from S5P on-board TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emission sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre- (February–March) and post- (October–January) monsoon periods. During the pre-monsoon period, the residential and transport sectors together account for more than 50 % of the total accumulated emissions, which are dominated by local sources (90 %) under easterly winds and by non-local sources (> 70 %) under north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this period). During the post-monsoon period non-local (60 %) transport emissions are the largest contributor to the total accumulated emissions as high emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary-layer aid the build-up of emissions along the trajectory path. Analysis of surface daily NO2 observations indicates that high pollution episodes (> 90th percentile) occur predominantly in the post-monsoon and more than 75 % of high pollution events are primarily caused by non-local sources. Overall, we find that in the post-monsoon period, there is a substantial import of NOx pollution into Delhi with a large contribution from the transport sector. This work indicates that the advection of highly polluted air originating from outside Delhi is of concern for the population and air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport sector, particularly during post-monsoon months. [ABSTRACT FROM AUTHOR]

A previous model intercomparison of the Tambora aerosol cloud has highlighted substantial differences among simulated volcanic aerosol properties in the pre-industrial stratosphere and has led to questions about the applicability of global aerosol models for large-magnitude explosive eruptions prior to the observational period. Here, we compare the evolution of the stratospheric aerosol cloud following the well-observed June 1991 Mt. Pinatubo eruption simulated with six interactive stratospheric aerosol microphysics models to a range of observational data sets. Our primary focus is on the uncertainties regarding initial SO 2 emission following the Pinatubo eruption, as prescribed in the Historical Eruptions SO 2 Emission Assessment experiments (HErSEA), in the framework of the Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP). Six global models with interactive aerosol microphysics took part in this study: ECHAM6-SALSA, EMAC, ECHAM5-HAM, SOCOL-AERv2, ULAQ-CCM, and UM-UKCA. Model simulations are performed by varying the SO 2 injection amount (ranging between 5 and 10 Tg S) and the altitude of injection (between 18–25 km). The comparisons show that all models consistently demonstrate faster reduction from the peak in sulfate mass burden in the tropical stratosphere. Most models also show a stronger transport towards the extratropics in the Northern Hemisphere, at the expense of the observed tropical confinement, suggesting a much weaker subtropical barrier in all the models, which results in a shorter e-folding time compared to the observations. Furthermore, simulations in which more than 5 Tg S in the form of SO 2 is injected show an initial overestimation of the sulfate burden in the tropics and, in some models, in the Northern Hemisphere and a large surface area density a few months after the eruption compared to the values measured in the tropics and the in situ measurements over Laramie. This draws attention to the importance of including processes such as the ash injection for the removal of the initial SO 2 and aerosol lofting through local heating. [ABSTRACT FROM AUTHOR]

Figures 6(a) and 6(b) in the study by L2022 used total ozone data from different sources (Total Ozone Mapping Spectrometer, Ozone Monitoring Instrument, and Ozone Mapping and Profiler Suite) without removing potential biases and drifts between the datasets, a step that is essential for accurate diagnosis of trends. [37] (hereafter L2022) used the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) to argue that there has been very substantial ozone depletion (>80%) in the tropical (30°S-30°N) lower stratosphere (LS) since the 1960s. OZONE IN THE TROPICAL LOWER STRATOSPHERE A. TOST data used by L2022 The World Ozone and Ultraviolet Radiation Data Center (WOUDC) website (https://woudc.org/data.php) hosts the TOST data in two formats: (1) ozonesonde data at 1 km intervals in altitude expanded with 4-day forward and backward trajectories to increase spatial sampling (hereafter labeled TOST RAW) and (2) a smoothed, gap-filled version of TOST RAW using linear interpolation of maps (hereafter labeled TOST SM). The stronger decline reported by L2022 is caused by inappropriate use of the gap-filled version of the TOST ozone dataset, which is based on sparse tropical ozone sondes before the 1990s. [Extracted from the article]

We report the recovery and re-calibration of an extensive dataset of vertical profile measurements of the 1963/64 stratospheric aerosol layer measured from a two-site searchlight measurement facility at White Sands missile base and Sacramento Peak observatory, in New Mexico, US. The recovered dataset comprises 105 profiles of 550nm aerosol extinction (βp(z)) and is part of a longer program of measurements with the US Air Force Cambridge Research Laboratories (AFCRL) searchlight facility that began in February 1963. The recovered series of βp(z) profiles span the 13-month period December 1963 to December 1964 and provide a unique record of the altitude and vertical extent of the Northern Hemisphere dispersed portion of the aerosol cloud from the March 1963 Agung volcanic eruption. The data recovery methodology involved first re-digitizing the 105 original βp(z) profiles (βp(z)) from individual Figures within an AFCRL research report (Elterman, 1966a). The re-calibration involves inverting the original equation used to compute βp(z) in Elterman (1966a; 1966b) to retrieve a normalized detector response (Er(z) Er(35)) profile for each of the 105 re-digitized βp 0(z) profiles. An iterative procedure was then used to compute the re-calibration βp(z) profiles (βp Ra(z)), with the molecular extinction profile calculated with the corresponding daily molecular extinction profile calculated from local soundings, rather than the US Standard Atmosphere 1962 in the original dataset. Two-way molecular and aerosol transmittance corrections are applied using the MODTRAN (MODerate resolution atmospheric TRANsmission) code in transmission mode, applying a best-estimate aerosol phase function calculated from measurements, applied for the entire 2.76 to 35.2 km column. For the tropospheric aerosol transmittance, the AERONET aerosol phase function from White Sands High Energy Laser Systems Test Facility (HELSTF) was applied (2.76 to 10.7 km), a separate stratospheric phase function applied between 11.2 and 35.2 km, calculated from a set of particle size distributions measured by the U-2 high-altitude aircraft over a US region in the vicinity of White Sands in early 1964. Errors were estimated taking as a reference the errors determined in the computation of βp 0(z). Using available tabulated data from the original procedure the errors in the re-digitalization of βp 0(z) and in the retrieval of the Er(z) Er(35) procedures were calculated and later added to the original estimates. Both the βp R(z) and the stratospheric aerosol optical depth magnitudes showed higher magnitudes than βp 0(z) and the original stratospheric aerosol optical depth, however their magnitudes show a reasonable agreement with other contemporary observations. Both the original and recalibration datasets are being submitted to PANGAEA data repository for its storage and public access. [ABSTRACT FROM AUTHOR]

We use TOMCAT, a 3-dimensional (3D) offline chemical transport model (CTM) forced by two different meteorological reanalysis data sets (ERA-Interim and ERA5) from the European Centre for Medium-Range weather Forecasts (ECMWF) to analyse seasonal behaviour and long-term trends in stratospheric ozone and mean age of air. The model-simulated ozone variations are evaluated against two observation-based data sets. For total column ozone (TCO) comparisons, we use the Copernicus Climate Change Service (C3S) data (1979–2019), while for ozone profiles we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set (1984–2019). We find that the CTM simulations forced by ERA-Interim (A_ERAI) and ERA5 (B_ERA5) can both successfully reproduce the spatial and temporal variations in stratospheric ozone. Also, modelled TCO anomalies from B_ERA5 show better agreement with C3S than A_ERAI, especially in Northern Hemisphere (NH) mid latitudes, except that it gives somewhat larger positive biases (> 15 DU, Dobson units) during winter–spring seasons. Ozone profile comparisons against SWOOSH data show larger differences between the two simulations. In the lower stratosphere, ozone differences can be directly attributed to the representation of dynamical processes, whereas in the upper stratosphere they can be directly linked to the differences in temperatures between ERAI and ERA5 data sets. Although TCO anomalies from B_ERA5 show relatively better agreement with C3S compared to A_ERAI, a comparison with SWOOSH data does not confirm that B_ERA5 performs better at simulating the variations in the stratospheric ozone profiles. We employ a multivariate regression model to quantify the TCO and ozone profile trends before and after peak stratospheric halogen loading in 1997. Our results show that, compared to C3S, TCO recovery trends (since 1998) in simulation B_ERA5 are significantly overestimated in the Southern Hemisphere (SH) mid latitudes, while for A_ERAI in the NH mid latitudes, simulated ozone trends remain negative. Similarly, in the lower stratosphere, B_ERA5 shows positive ozone recovery trends for both NH and SH mid latitudes. In contrast, both SWOOSH and A_ERAI show opposite (negative) trends in the NH mid latitudes. Furthermore, we analyse age of air (AoA) trends to diagnose transport differences between the two reanalysis data sets. Simulation B_ERA5 shows a positive AoA trend after 1998 and somewhat older age in the NH lower stratosphere compared to A_ERAI, indicating that a slower Brewer–Dobson circulation does not translate into reduced wintertime ozone buildup in the NH extratropical lower stratosphere. Overall, our results show that models forced by the most recent ERA5 reanalyses may not yet be capable of reproducing observed changes in stratospheric ozone, particularly in the lower stratosphere. [ABSTRACT FROM AUTHOR]

We use the TOMCAT 3-dimensional (3D) off-line chemical transport model (CTM) forced by two different meteorological reanalysis datasets (ERA-Interim and ERA5) from the European Centre for Medium-Range weather Forecasts (ECMWF) to study stratospheric ozone trends and variability. The model-simulated ozone variations are evaluated against two observation-based data sets. For total column ozone (TCO) we use the Copernicus Climate Change Service (C3S) data (1979-2019), while for ozone profiles we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) database (1984-2019). We find that the CTM simulations forced by ERA-Interim (A_ERAI) and ERA5 (B_ERA5) can both successfully reproduce spatial and temporal variations in stratospheric ozone. Modelled TCO anomalies from B_ ERA5 show better agreement with C3S than A_ERAI, especially in northern hemisphere (NH) mid-latitudes, except that it produces large positive biases (> 15 DU) during winter-spring seasons. Ozone profile comparisons against SWOOSH data show larger differences between the two simulations. In the lower stratosphere, which controls the TCO, these are primarily due to differences in transport, whereas in the upper stratosphere they can be directly attributed to the differences in temperatures between the two reanalysis data sets. Although TCO anomalies from B_ERA5 show better agreement with C3S compared to A_ERAI, comparison with SWOOSH data does not confirm that B_ERA5 performs better in simulating the stratospheric ozone profiles. We employ a multi-variate regression model with piecewise linear trends (PWLT) to quantify ozone trends before and after peak stratospheric halogen loading in 1997. This model shows that compared to C3S, TCO recovery trends (since 1998) in simulation B_ERA5 are significantly overestimated in the southern hemisphere (SH) midlatitudes, while for A_ERAI in the NH mid-latitudes simulated ozone trends remain negative. Similarly, in the lower stratosphere B_ERA5 shows positive ozone recovery trends for both NH and SH mid-latitudes. In contrast, both SWOOSH and A_ERAI show opposite (negative) trends in the NH mid-latitudes. We analyse Age-of-Air (AoA) trends to diagnose transport differences between the two reanalysis data sets. Simulation B_ERA5 shows a positive AoA trend after 1998 and somewhat older age in the NH lower stratosphere compared to A_ERAI, indicating a slower Brewer-Dobson circulation does not translate into reduced wintertime ozone build-up in the NH extratropical lower stratosphere. Overall, our results show that models forced by the most recent ERA5 reanalyses may not yet be capable of reproducing observed changes in stratospheric ozone, particularly in the lower stratosphere. [ABSTRACT FROM AUTHOR]

Until now our understanding of the 11-year solar cycle signal (SCS) in stratospheric ozone has been largely based on high-quality but sparse ozone profiles from the Stratospheric Aerosol and Gas Experiment (SAGE) II or coarsely resolved ozone profiles from the nadir-viewing Solar Backscatter Ultraviolet Radiometer (SBUV) satellite instruments. Here, we analyse 16 years (2005–2020) of ozone profile measurements from the Microwave Limb Sounder (MLS) instrument on the Aura satellite to estimate the 11-year SCS in stratospheric ozone. Our analysis of Aura-MLS data suggests a single-peak-structured SCS profile (about 3 % near 4 hPa or 40 km) in tropical stratospheric ozone, which is significantly different to the SAGE II and SBUV-based double-peak-structured SCS. We also find that MLS-observed ozone variations are more consistent with ozone from our control model simulation that uses Naval Research Laboratory (NRL) v2 solar fluxes. However, in the lowermost stratosphere modelled ozone shows a negligible SCS compared to about 1 % in Aura-MLS data. An ensemble of ordinary least squares (OLS) and three regularised (lasso, ridge and elastic net) linear regression models confirms the robustness of the estimated SCS. In addition, our analysis of MLS and model simulations shows a large SCS in the Antarctic lower stratosphere that was not seen in earlier studies. We also analyse chemical transport model simulations with alternative solar flux data. We find that in the upper (and middle) stratosphere the model simulation with Solar Radiation and Climate Experiment (SORCE) satellite solar fluxes is also consistent with the MLS-derived SCS and agrees well with the control simulation and one which uses Spectral and Total Irradiance Reconstructions (SATIRE) solar fluxes. Hence, our model simulation suggests that with recent adjustments and corrections, SORCE data can be used to analyse effects of solar flux variations. Furthermore, analysis of a simulation with fixed solar fluxes and one with fixed (annually repeating) meteorology confirms that the implicit dynamical SCS in the (re)analysis data used to force the model is not enough to simulate the observed SCS in the middle and upper stratospheric ozone. Finally, we argue that the overall significantly different SCS compared to previous estimates might be due to a combination of different factors such as much denser MLS measurements, almost linear stratospheric chlorine loading changes over the analysis period, variations in the stratospheric dynamics as well as relatively unperturbed stratospheric aerosol layer that might have influenced earlier analyses. [ABSTRACT FROM AUTHOR]

Figure compares TOMCAT and ML-TOMCAT profiles with the three altitude-based ozone data sets with a focus on the Equator (0 HT ht latitude). The bottom panel shows the ozone sub-column over the Antarctic (poleward of 70 HT ht S latitude) integrated between 12 and 20 km for TOMCAT, ML-TOMCAT and MLS averaged over September-October months. We use ozone profile output from a CTM to create a machine-learning-based satellite-corrected long-term chemically (and dynamically) consistent ozone profile data set (hereafter, ML-TOMCAT) for the 1979-2020 time period. [Extracted from the article]

We report the recovery and processing methodology of the first ever multi-year lidar dataset of the stratospheric aerosol layer. A Q-switched ruby lidar measured 66 vertical profiles of 694 nm attenuated backscatter at Lexington, Massachusetts, between January 1964 and August 1965, with an additional nine profile measurements conducted from College, Alaska, during July and August 1964. We describe the processing of the recovered lidar backscattering ratio profiles to produce mid-visible (532 nm) stratospheric aerosol extinction profiles (sAEP 532) and stratospheric aerosol optical depth (sAOD 532) measurements, utilizing a number of contemporary measurements of several different atmospheric variables. Stratospheric soundings of temperature and pressure generate an accurate local molecular backscattering profile, with nearby ozone soundings determining the ozone absorption, which are used to correct for two-way ozone transmittance. Two-way aerosol transmittance corrections are also applied based on nearby observations of total aerosol optical depth (across the troposphere and stratosphere) from sun photometer measurements. We show that accounting for these two-way transmittance effects substantially increases the magnitude of the 1964/1965 stratospheric aerosol layer's optical thickness in the Northern Hemisphere mid-latitudes, then ∼ 50 % larger than represented in the Coupled Model Intercomparison Project 6 (CMIP6) volcanic forcing dataset. Compared to the uncorrected dataset, the combined transmittance correction increases the sAOD 532 by up to 66 % for Lexington and up to 27 % for Fairbanks, as well as individual sAEP 532 adjustments of similar magnitude. Comparisons with the few contemporary measurements available show better agreement with the corrected two-way transmittance values. Within the January 1964 to August 1965 measurement time span, the corrected Lexington sAOD 532 time series is substantially above 0.05 in three distinct periods, October 1964, March 1965, and May–June 1965, whereas the 6 nights the lidar measured in December 1964 and January 1965 had sAOD values of at most ∼ 0.03. The comparison with interactive stratospheric aerosol model simulations of the Agung aerosol cloud shows that, although substantial variation in mid-latitude sAOD 532 are expected from the seasonal cycle in the stratospheric circulation, the Agung cloud's dispersion from the tropics would have been at its strongest in winter and weakest in summer. The increasing trend in sAOD from January to July 1965, also considering the large variability, suggests that the observed variations are from a different source than Agung, possibly from one or both of the two eruptions that occurred in 1964/1965 with a Volcanic Explosivity Index (VEI) of 3: Trident, Alaska, and Vestmannaeyjar, Heimaey, south of Iceland. A detailed error analysis of the uncertainties in each of the variables involved in the processing chain was conducted. Relative errors for the uncorrected sAEP 532 were 54 % for Fairbanks and 44 % Lexington. For the corrected sAEP 532 the errors were 61 % and 64 %, respectively. The analysis of the uncertainties identified variables that with additional data recovery and reprocessing could reduce these relative error levels. Data described in this work are available at 10.1594/PANGAEA.922105 (Antuña-Marrero et al., 2020a). [ABSTRACT FROM AUTHOR]

Until now our understanding of the 11-year solar cycle signal (SCS) in stratospheric ozone has been largely based on high quality but sparse ozone profiles from the Stratospheric Aerosol and Gas Experiment (SAGE) II or coarsely resolved ozone profiles from the nadir-viewing Solar Backscatter Ultraviolet Radiometer (SBUV) satellite instruments. Here, we analyse 16 years (2005-2020) of ozone profile measurements from the Microwave Limb Sounder (MLS) instrument on the Aura satellite to estimate the 11-year SCS in stratospheric ozone. Our analysis of Aura-MLS data suggests a single-peak-structured SCS profile (about 3% near 4 hPa or 40 km) in tropical stratospheric ozone, which is significantly different to the SAGE II and SBUV-based double-peak-structured SCS. We also find that MLS-observed ozone variations are more consistent with ozone from our control model simulation that uses Naval Research Laboratory (NRL) v2 solar fluxes. However, in the lowermost stratosphere modelled ozone shows a negligible SCS compared to about 1% in Aura-MLS data. An ensemble of Ordinary Least Square (OLS) and three regularised (Lasso, Ridge and ElasticNet) linear regression models confirms the robustness of the estimated SCS. Our analysis of MLS and model simulations also shows a large SCS in the Antarctic lower stratosphere that was not seen in earlier studies. We also analyse chemical transport model simulations with alternative solar flux data. We find that in the upper (and middle) stratosphere the model simulation with Solar Radiation and Climate Experiment (SORCE) satellite solar fluxes is also consistent with the MLS-derived SCS and agrees well with the control simulation and one which uses Spectral and Total Irradiance Reconstructions (SATIRE) solar fluxes. Hence, our model simulation suggests that with recent adjustments and corrections, SORCE solar fluxes can be used to analyse effects of solar flux variations. Finally, we argue that the overall significantly different SCS compared to earlier estimates might be due to a combination of different factors such as much denser MLS measurements, almost linear stratospheric chlorine loading changes over the analysis period, as well as a stratospheric aerosol layer relatively unperturbed by major volcanic eruptions. [ABSTRACT FROM AUTHOR]

High quality stratospheric ozone profile datasets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments obtain stratospheric ozone profile measurements over typical mission durations of 5-15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create longer term observation-based ozone profile datasets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different datasets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters. Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a Random-Forest (RF) ensemble learning method to create a merged 42-year (1979-2020) stratospheric ozone profile dataset (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gas, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991-1998) and Aura (2005-2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based datasets which use pressure as the vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the datasets which are height-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties in the observational datasets. The ML-TOMCAT dataset is thus ideally suited for the evaluation of model ozone profiles from the tropopause to 0.1 hPa. ML-TOMCAT data is freely available via https://zenodo.org/record/4997959#.YNzleUlKiUk (Dhomse et al., 2021). [ABSTRACT FROM AUTHOR]

In the winter and spring of 2019/2020, the unusually cold, strong, and stable polar vortex created favorable conditions for ozone depletion in the Arctic. Chemical ozone loss started earlier than in any previous year in the satellite era and continued until late March, resulting in the unprecedented reduction of the ozone column. The vortex was located above the Polar Environment Atmospheric Research Laboratory in Eureka, Canada (80°N, 86°W) from late February to the end of April, presenting an excellent opportunity to examine ozone loss from a single ground station. Measurements from a suite of instruments show that total column ozone was at an all‐time low in the 20‐year data set, 22–102 DU below previous records set in 2011. Ozone minima (<200 DU), enhanced OClO and BrO slant columns, and unusually low‐HCl, ClONO2, and HNO3 columns were observed in March. Polar stratospheric clouds were present as late as 20 March, and ozonesondes show unprecedented depletion in the March and April profiles (to <0.2 ppmv). While both chemical and dynamical factors lead to reduced ozone when the vortex is cold, the contribution of chemical depletion (based on the variable correlation of ozone and temperature) was exceptional in spring 2020 when compared to typical Arctic winters. Mean chemical ozone loss over Eureka was estimated to be 111–126 DU (27%–31%) using April measurements and passive ozone from the SLIMCAT chemical transport model. While absolute ozone loss was generally smaller in 2020 than in 2011, percentage ozone loss was greater in 2020. Plain Language Summary: While an ozone hole forms over Antarctica every year, the Arctic typically does not experience such dramatic ozone loss. The chlorine and bromine (halogen) reactions that destroy ozone require very low temperatures that are rarely observed in the Arctic stratosphere. The winter and spring of 2019/2020, however, was unusually cold in the Arctic, and consequently, a large amount of ozone was destroyed by halogen chemistry. To understand the behavior of ozone in spring 2020, we use measurements from the Polar Environment Atmospheric Research Laboratory in Eureka, Canada. Eureka (at 80°N) is one of the northernmost research stations in the world, and thus an ideal location to observe ozone loss. Spring 2020 ozone minima were lower than any in the 20‐year data set, and ozone destruction was ongoing until the end of March, which is rare in the Arctic. While ozone concentrations are largely determined by circulation patterns in the Arctic stratosphere, chemistry in spring 2020 was a much larger factor than usual. Halogen chemistry destroyed 27%–31% of the total ozone, compared to about 10% in a typical winter. The only year on record with comparable ozone loss is 2011, and a larger percentage of the ozone column was lost in 2020. Key Points: Record low ozone columns (<200 DU) were observed over Eureka in spring 2020Limited dynamical resupply of ozone and chemical destruction both contributed to reduced ozone columnsMean chemical ozone loss of 111–126 DU (27%–31%) represents similar absolute loss and greater relative loss compared to that in spring 2011 [ABSTRACT FROM AUTHOR]

The phase out of anthropogenic ozone‐depleting substances such as chlorofluorocarbons under the terms of the Montreal Protocol led to the development and worldwide use of hydrofluorocarbons (HFCs) in refrigeration, air conditioning, and as blowing agents and propellants. Consequently, over recent years, the atmospheric abundances of HFCs have dramatically increased. HFCs are powerful greenhouse gases and are now controlled under the terms of the 2016 Kigali Amendment to the Montreal Protocol. HFC‐134a is currently the most abundant HFC in the atmosphere, breaking the 100 ppt barrier in 2018, and can be measured in the Earth's atmosphere by the satellite remote‐sensing instrument ACE‐FTS (Atmospheric Chemistry Experiment‐Fourier Transform Spectrometer), which has been measuring since 2004. This work uses the ACE‐FTS v4.0 data product to investigate global distributions and trends of HFC‐134a. These measurements are compared with a simulation of SLIMCAT, a state‐of‐the‐art three‐dimensional chemical transport model, which is constrained by global surface HFC‐134a measurements. The agreement between observation and model is good, although in the tropical troposphere ACE‐FTS measurements are biased low by up to 10–15 ppt. The overall ACE‐FTS global trend of HFC‐134a for the altitude range 5.5–24.5 km and 2004–2018 time period is approximately linear with a value of 4.49 ± 0.02 ppt/year, slightly lower than the corresponding SLIMCAT trend of 4.66 ppt/year. Using a simple box model, we also estimate the annual global emissions and burdens of HFC‐134a from the model data, indicating that emissions of HFC‐134a have increased almost linearly, reaching 236 Gg by 2018. Key Points: First comparison between long‐term satellite measurements (ACE‐FTS) of atmospheric HFC‐134a and a 3D chemical transport model (SLIMCAT)Observations agree well with SLIMCAT, although ACE‐FTS is biased low by up to 10–15 ppt in the troposphere between 30°S and 30°NGlobal trends are linear to a good approximation: 4.49 ppt/year for ACE‐FTS (2004–2018; 5.5–24.5 km) and 4.66 ppt/year for SLIMCAT [ABSTRACT FROM AUTHOR]

Satellite observations of relevant trace gases and meteorological data from ERA5 were used to describe the dynamics and chemistry of the spectacular Arctic 2019/20 winter/spring season. Exceptionally low total ozone values of slightly less than 220 DU were observed in mid‐March within an unusually large stratospheric polar vortex. Very high OClO and very low NO2 column amounts observed by GOME‐2A were indicative of unusually large active chlorine levels and significant denitrification, which likely contributed to large chemical ozone loss. Using results from the TOMCAT chemical transport model (CTM) and ozone observations from S5P/TROPOMI, GOME‐2 (total column), SCIAMACHY, and OMPS‐LP (vertical profiles), chemical ozone loss was evaluated and compared with the previous record Arctic winter 2010/11. The polar‐vortex‐averaged total column ozone loss in 2019/20 reached 88 DU (23%) and 106 DU (28%) based upon observations and model, respectively, by the end of March, which was similar to that derived for 2010/11. The maximum ozone loss (∼80%) observed by OMPS‐LP was near the 450 K potential temperature level (∼18 km altitude). Because of the larger polar vortex area in March 2020 compared to March 2011 (about 25% at 450 K), ozone mass loss was larger in Arctic winter 2019/20. It is shown that Arctic cap temperatures were at a record 40‐year low in winter, suggesting that Arctic cold winters are getting colder. Tropical UTLS temperatures show an apparent upward trend during the last two decades that may indicate a weakening of the Brewer‐Dobson circulation and strengthening of the polar vortex. Key Points: Very large OClO and very low NO2 slant columns were observed by GOME‐2A during Arctic winter 2019/20Chemical total column ozone loss of 88 DU and 106 DU was derived from TROPOMI satellite observations and the chemical transport modelChemical ozone loss derived from OMPS‐LP satellite data reached 2.1 ppmv (80%) near the 450 K potential temperature level (∼18 km) [ABSTRACT FROM AUTHOR]

We have estimated the spatial changes in NO 2 levels over different regions of India during the COVID-19 lockdown (25 March–3 May 2020) using the satellite-based tropospheric column NO 2 observed by the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), as well as surface NO 2 concentrations obtained from the Central Pollution Control Board (CPCB) monitoring network. A substantial reduction in NO 2 levels was observed across India during the lockdown compared to the same period during previous business-as-usual years, except for some regions that were influenced by anomalous fires in 2020. The reduction (negative change) over the urban agglomerations was substantial (∼ 20 %–40 %) and directly proportional to the urban size and population density. Rural regions across India also experienced lower NO 2 values by ∼ 15 %–25 %. Localised enhancements in NO 2 associated with isolated emission increase scattered across India were also detected. Observed percentage changes in satellite and surface observations were consistent across most regions and cities, but the surface observations were subject to larger variability depending on their proximity to the local emission sources. Observations also indicate NO 2 enhancements of up to ∼ 25 % during the lockdown associated with fire emissions over the north-east of India and some parts of the central regions. In addition, the cities located near the large fire emission sources show much smaller NO 2 reduction than other urban areas as the decrease at the surface was masked by enhancement in NO 2 due to the transport of the fire emissions. [ABSTRACT FROM AUTHOR]

As part of the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP), several climate modeling centers performed a coordinated pre-study experiment with interactive stratospheric aerosol models simulating the volcanic aerosol cloud from an eruption resembling the 1815 Mt. Tambora eruption (VolMIP-Tambora ISA ensemble). The pre-study provided the ancillary ability to assess intermodel diversity in the radiative forcing for a large stratospheric-injecting equatorial eruption when the volcanic aerosol cloud is simulated interactively. An initial analysis of the VolMIP-Tambora ISA ensemble showed large disparities between models in the stratospheric global mean aerosol optical depth (AOD). In this study, we now show that stratospheric global mean AOD differences among the participating models are primarily due to differences in aerosol size, which we track here by effective radius. We identify specific physical and chemical processes that are missing in some models and/or parameterized differently between models, which are together causing the differences in effective radius. In particular, our analysis indicates that interactively tracking hydroxyl radical (OH) chemistry following a large volcanic injection of sulfur dioxide (SO2) is an important factor in allowing for the timescale for sulfate formation to be properly simulated. In addition, depending on the timescale of sulfate formation, there can be a large difference in effective radius and subsequently AOD that results from whether the SO2 is injected in a single model grid cell near the location of the volcanic eruption, or whether it is injected as a longitudinally averaged band around the Earth. [ABSTRACT FROM AUTHOR]

We use a three‐dimensional chemical transport model and satellite observations to investigate Arctic ozone depletion in winter/spring 2019/20 and compare with earlier years. Persistently, low temperatures caused extensive chlorine activation through to March. March‐mean polar‐cap‐mean modeled chemical column ozone loss reached 78 DU (local maximum loss of ∼108 DU in the vortex), similar to that in 2011. However, weak dynamical replenishment of only 59 DU from December to March was key to producing very low (<220 DU) column ozone values. The only other winter to exhibit such weak transport in the past 20 years was 2010/11, so this process is fundamental to causing such low ozone values. A model simulation with peak observed stratospheric total chlorine and bromine loading (from the mid‐1990s) shows that gradual recovery of the ozone layer over the past 2 decades ameliorated the polar cap ozone depletion in March 2020 by ∼20 DU. Plain Language Summary: Ozone depletion in the polar stratosphere is caused by chlorine and bromine species which are activated by low temperatures. Chlorine and bromine are transported to the stratosphere following the surface emission of ozone‐depleting substances (ODSs). While springtime ozone depletion in the Antarctic is almost always large, it is much more variable in the Arctic due to warmer temperatures and more disturbed stratospheric dynamics. Using a three‐dimensional atmospheric chemical transport model and satellite observations, we show that the very low ozone columns observed in March 2020 were a consequence of large chemical destruction and weaker‐than‐normal replenishment by transport. These very low ozone levels are, by some measures, record values despite 2 decades of decreasing stratospheric chlorine and bromine through controls of the Montreal Protocol. Had the meteorology of 2019/20 occurred 2 decades ago, the ozone loss would have been notably larger. The Arctic stratospheric dynamics for 2019/20 are extreme relative to the past 2 decades but fit a compact relationship that links column ozone variations over Arctic and Antarctic winters. Key Points: Large mean Arctic (>63°N) chemical ozone destruction in 2019/20 of 78 DU, similar to other extreme cold winters in the past 2 decadesAnomalously weak wintertime dynamical replenishment of only ∼60 DU contributed strongly to the very low observed ozone column in MarchOzone recovery caused 20 DU less mean Arctic ozone loss in March 2020 than would have occurred with stratospheric halogens at 1995 levels [ABSTRACT FROM AUTHOR]

Accurately quantifying volcanic impacts on climate is a key requirement for robust attribution of anthropogenic climate change. Here we use the Unified Model – United Kingdom Chemistry and Aerosol (UM-UKCA) composition–climate model to simulate the global dispersion of the volcanic aerosol clouds from the three largest eruptions of the 20th century: 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo. The model has interactive stratospheric chemistry and aerosol microphysics, with coupled aerosol–radiation interactions for realistic composition–dynamics feedbacks. Our simulations align with the design of the Interactive Stratospheric Aerosol Model Intercomparison (ISA-MIP) "Historical Eruption SO2 Emissions Assessment". For each eruption, we perform three-member ensemble model experiments for upper, mid-point, and lower estimates of SO2 emission, each re-initialised from a control run to approximately match the observed transition in the phase of the quasi-biennial oscillation (QBO) in the 6 months after the eruptions. With this experimental design, we assess how each eruption's emitted SO2 translates into a tropical reservoir of volcanic aerosol and analyse the subsequent dispersion to mid-latitudes. We compare the simulations to the volcanic forcing datasets (e.g. Space-based Stratospheric Aerosol Climatology (GloSSAC); , and) that are used in historical integrations for the two most recent Coupled Model Intercomparison Project (CMIP) assessments. For Pinatubo and El Chichón, we assess the vertical extent of the simulated volcanic clouds by comparing modelled extinction to the Stratospheric Aerosol and Gas Experiment (SAGE-II) v7.0 satellite measurements and to 1964–1965 Northern Hemisphere ground-based lidar measurements for Agung. As an independent test for the simulated volcanic forcing after Pinatubo, we also compare simulated shortwave (SW) and longwave (LW) top-of-the-atmosphere radiative forcings to the flux anomalies measured by the Earth Radiation Budget Experiment (ERBE) satellite instrument. For the Pinatubo simulations, an injection of 10 to 14 Tg SO2 gives the best match to the High Resolution Infrared Sounder (HIRS) satellite-derived global stratospheric sulfur burden, with good agreement also with SAGE-II mid-visible and near-infra-red extinction measurements. This 10–14 Tg range of emission also generates a heating of the tropical stratosphere that is consistent with the temperature anomaly present in the ERA-Interim reanalysis. For El Chichón, the simulations with 5 and 7 Tg SO2 emission give best agreement with the observations. However, these simulations predict a much deeper volcanic cloud than represented in the GloSSAC dataset, which is largely based on an interpolation between Stratospheric Aerosol Measurements (SAM-II) satellite and aircraft measurements. In contrast, these simulations show much better agreement during the SAGE-II period after October 1984. For 1963 Agung, the 9 Tg simulation compares best to the forcing datasets with the model capturing the lidar-observed signature of the altitude of peak extinction descending from 20 km in 1964 to 16 km in 1965. Overall, our results indicate that the downward adjustment to SO2 emission found to be required by several interactive modelling studies when simulating Pinatubo is also needed when simulating the Agung and El Chichón aerosol clouds. This strengthens the hypothesis that interactive stratospheric aerosol models may be missing an important removal or re-distribution process (e.g. effects of co-emitted ash) which changes how the tropical reservoir of volcanic aerosol evolves in the initial months after an eruption. Our model comparisons also identify potentially important inhomogeneities in the CMIP6 dataset for all three eruption periods that are hard to reconcile with variations predicted in the interactive stratospheric aerosol simulations. We also highlight large differences between the CMIP5 and CMIP6 volcanic aerosol datasets for the Agung and El Chichón periods. Future research should aim to reduce this uncertainty by reconciling the datasets with additional stratospheric aerosol observations. [ABSTRACT FROM AUTHOR]

We have estimated the spatial changes in NO2 levels over different regions of India during the COVID-19 lockdown (25th March-3rd May 2020) using the satellite-based tropospheric column NO2 observed by the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), as well as surface NO2 concentrations obtained from the Central Pollution Control Board (CPCB) monitoring network. A substantial reduction in NO2 levels was observed across India during the lockdown compared to the same period during previous business-as-usual years, except for some regions that were influenced by anomalous fires in 2020. The reduction (negative change) over the urban agglomerations was substantial (∼ 20-40 %) and directly proportional to the urban size and population density. Rural regions across India also experienced lower NO2 values by ∼ 15-25 %. Localised enhancement of NO2 associated with isolated emission increase scattered across India, were also detected. Observed percentage changes in satellite and surface observations were consistent across most regions and cities, but the surface observations were subject to larger variability depending on their proximity to the local emission sources. Observations also indicate NO2 enhancements of up to ∼ 25 % during the lockdown associated with fire emissions over the north-east, and some parts of central regions. In addition, the cities located near the large fire emission sources show much smaller NO2 reduction than other urban areas as the decrease at the surface was masked by enhancement in NO2 due to the transport of the fire emissions. [ABSTRACT FROM AUTHOR]

As part of the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP), several climate modeling centers performed a coordinated pre-study experiment with interactive stratospheric aerosol models simulating the volcanic aerosol cloud from an eruption resembling the 1815 Mt Tambora eruption (VolMIP-Tambora ISA ensemble). The pre-study provided the ancillary ability to assess intermodel diversity in the radiative forcing for a large stratospheric-injecting equatorial eruption when the volcanic aerosol cloud is simulated interactively. An initial analysis of the VolMIP-Tambora ISA ensemble showed large disparities between models in the stratospheric global mean aerosol optical depth (AOD). In this study, we now show that stratospheric global mean AOD differences among the participating models are primarily due to differences in aerosol size, which we track here by effective radius. We identify specific physical and chemical processes that are missing in some models and/or parameterized differently between models, which are together causing the differences in effective radius. In particular, our analysis indicates that interactively tracking hydroxyl radical (OH) chemistry following a large volcanic injection of sulfur dioxide (SO2) is an important factor in allowing for the timescale for sulfate formation to be properly simulated. In addition, depending on the timescale of sulfate formation, there can be a large difference in effective radius and subsequently AOD that results from whether the SO2 is injected in a single model gridcell near the location of the volcanic eruption, or whether it is injected as a longitudinally averaged band around the Earth. [ABSTRACT FROM AUTHOR]

Various observation-based datasets have confirmed positive zonal mean column ozone trends at midlatitudes as a result of the successful implementation of the Montreal Protocol. However, there is still uncertainty about the longitudinal variation of these trends and the direction and magnitude of ozone changes at low latitudes. Here, we use the extended Copernicus Climate Change Service (C3S) dataset (1979–2017) to investigate the long-term variations in total column ozone (TCO) over the Tibetan Plateau (TP) for different seasons. We use piecewise linear trend (PWLT) and equivalent effective stratospheric chlorine loading (EESC)-based multivariate regression models with various proxies to attribute the influence of dynamical and chemical processes on the TCO variability. We also compare the seasonal behaviour of the relative total ozone low (TOL) over the TP with the zonal mean at the same latitude. Both regression models show that the TP column ozone trends change from negative trends from 1979 to 1996 to small positive trends from 1997 to 2017, although the later positive trend based on PWLT is not statistically significant. The wintertime positive trend starting from 1997 is larger than that in summer, but both seasonal TP recovery rates are smaller than the zonal means over the same latitude band. For TP column ozone, both regression models suggest that the geopotential height at 150 hPa (GH150) is a more suitable and realistic dynamical proxy compared to a surface temperature proxy used in some previous studies. Our analysis also shows that the wintertime GH150 plays an important role in determining summertime TCO over the TP through persistence of the ozone signal. For the zonal mean column ozone at this latitude, the quasi-biennial oscillation (QBO) is nonetheless the dominant dynamical proxy. We also use a 3-D chemical transport model to diagnose the contributions of different proxies for the TP region. The role of GH150 variability is illustrated by using two sensitivity experiments with repeating dynamics of 2004 and 2008. The simulated ozone profiles clearly show that wintertime TP ozone concentrations are largely controlled by tropics to midlatitude pathways, whereas in summer variations associated with tropical processes play an important role. These model results confirm that the long-term trends of TCO over the TP are dominated by different processes in winter and summer. The different TP recovery rates relative to the zonal means at the same latitude band are largely determined by wintertime dynamical processes. [ABSTRACT FROM AUTHOR]

Ozone profile measurements collected at L'Aquila (Italy, 42.4° N) during seventeen years of radio-sounding (2000-2016) are presented here, with an analysis of derived trends. Model results from the SPARC-CCMI exercise are used in parallel to highlight the physical and chemical mechanisms regulating mid-latitude ozone trends. The statistically significant trends highlighted in time series at L'Aquila are those in the mid-upper stratosphere (+5.9 ± 4.2), mid troposphere (+5.9 ± 2.4) and upper troposphere (+2.5 ± 0.9), all in percent/decade. The upper stratospheric positive trend was already well documented in recent WMO assessments and attributed to the starting decline of stratospheric Cly and Bry and to the stratospheric cooling induced by increasing well mixed greenhouse gases, thus slowing down gas-phase reactions that destroy ozone in the upper stratosphere. The ozone increase in the mid-upper troposphere is largely regulated by the increasing strength of the Brewer-Dobson circulation, which moves more ozone from the tropics to the extratropics and enhances the tropospheric influx from the lowermost stratosphere. This climate feedback mechanism on tropospheric ozone is only partially compensated by the increasing chemical ozone loss associated to higher H2O values in response to the tropospheric warming. We also note that ozone trends obtained in the lower stratosphere are negative (-2.2 percent/decade), but do not result to be statistically significant in our analyses. [ABSTRACT FROM AUTHOR]

Accurate quantification of the effects of volcanic eruptions on climate is a key requirement for better attribution of anthropogenic climate change. Here we use the UM-UKCA composition-climate model to simulate the atmospheric evolution of the volcanic aerosol clouds from the three largest eruptions of the 20th century: 1963 Agung, 1982 El Chichón and 1991 Pinatubo. The model has interactive stratospheric chemistry and aerosol microphysics, with coupled aerosol-radiation interactions for realistic composition-dynamics feedbacks. Our simulations align with the design of the Interactive Stratospheric Aerosol Model Intercomparison (ISA-MIP) "Historical Eruption SO2 Emissions Assessment". For each eruption, we perform 3-member ensemble model experiments with upper, mid-point and lower estimates for SO2 emission, each initialised to a meteorological state to match the observed phase of the quasi-biennial oscillation (QBO) at the times of the eruptions. We assess how each eruption's emitted SO2 evolves into a tropical reservoir of volcanic aerosol and analyse the subsequent dispersion to mid-latitudes. We compare the simulations to the three volcanic forcing datasets used in historical integrations for the two most recent Coupled Model Intercomparison Project (CMIP) assessments: the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) for CMIP6, and the Sato et al. (1993) and Ammann et al. (2003) datasets used in CMIP5. We also assess the vertical extent of the volcanic aerosol clouds by comparing simulated extinction to Stratospheric Aerosol and Gas Experiment II (SAGE-II) v7.0 satellite aerosol data (1985-1995) for Pinatubo and El Chichón, and to 1964-65 northern hemisphere ground-based lidar measurements for Agung. As an independent test for the simulated volcanic forcing after Pinatubo, we also compare to the shortwave (SW) and longwave (LW) Top-of-the-Atmosphere flux anomalies measured by the Earth Radiation Budget Experiment (ERBE) satellite instrument. For the Pinatubo simulations, an injection of 10 to 14 Tg SO2 gives the best match to the High Resolution Infrared Sounder (HIRS) satellite-derived global stratospheric sulphur burden, with good agreement also to SAGE-II mid-visible and near-infrared extinction measurements. This 10-14 Tg range of emission also generates a heating of the tropical stratosphere that is comparable with the temperature anomaly seen in the ERA-Interim reanalyses. For El Chichón the simulations with 5 Tg and 7 Tg SO2 emission give best agreement with the observations. However, these runs predict a much deeper volcanic cloud than present in the CMIP6 data, with much higher aerosol extinction than the GloSSAC data up to October 1984, but better agreement during the later SAGE-II period. For 1963 Agung, the 9 Tg simulation compares best to the forcing datasets with the model capturing the lidar-observed signature of peak extinction descending from 20 km in 1964 to 16 km in 1965. Overall, our results indicate that the downward adjustment to previous SO2 emission estimates for Pinatubo as suggested by several interactive modelling studies is also needed for the Agung and El Chichón aerosol clouds. This strengthens the hypothesis that interactive stratospheric aerosol models may be missing an important removal or redistribution process (e.g. effects of co-emitted ash) which changes how the tropical reservoir of volcanic aerosol evolves in the initial months after an eruption. Our analysis identifies potentially important inhomogeneities in the CMIP6 dataset for all three periods that are hard to reconcile with variations predicted by the interactive stratospheric aerosol model. We also highlight large differences between the CMIP5 and CMIP6 volcanic aerosol datasets for the Agung and El Chichón periods. Future research should aim to reduce this uncertainty by reconciling the datasets with additional stratospheric aerosol observations. [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]

We use the ozone dataset from the Copernicus Climate Change Service (C3S) during 1979-2017 to investigate the long-term variations of the total column ozone (TCO) and the relative total ozone low (TOL) over the Tibetan Plateau (TP) during different seasons. Based on various regression models, the wintertime TCO over the TP decreases overall during 1979-2017 with ongoing decreases since 1997. We perform multivariate regression analysis to quantify the influence of dynamical and chemical processes responsible for the long-term TCO variability over the TP. We use both piecewise linear trend (PWLT) and equivalent effective stratospheric chlorine loading (EESC)-based regression models that include explanatory variables such as the 11-year solar cycle, quasi-biennial oscillation (QBO) at 30 hPa and 10 hPa and the geopotential height (GH) at 150 hPa. The 150 hPa GH is found to be a major dynamical contributor to the total ozone variability (8 %) over the TP in wintertime. We also find strong correlation between TCO in DJF and the following JJA, indicating that negative/positive anomalies in the wintertime build up persist into summer. We also use the TOMCAT/SLIMCAT 3-D chemical transport model to investigate the contributions of different factors to the ozone variations over the TP. Using identical regression model on simulated TCO time series, we obtain consistent results with C3S-based data. We perform two sensitivity experiments with repeating dynamics of 2004 and 2008 to further study the role that the GH at 150 hPa plays in the ozone variations over the TP. The GH differences between the two years show an obvious, negative centre near 150 hPa over the TP in DJF. Composite analysis show that GH fluctuations associated with Inter Tropical Convergence Zone, ENSO events or Walker circulation play a key role in controlling TCO variability in the lower stratosphere. [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 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]

The radiative forcing caused by a volcanic eruption is dependent on several eruption source parameters such as the mass of sulfur dioxide (SO2) emitted, the eruption column height, and the eruption latitude. General circulation models with prognostic aerosol and chemistry schemes can be used to investigate how each parameter influences the volcanic forcing. However, the range of multidimensional parameter space that can be explored is restricted because such simulations are computationally expensive. Here we use statistical emulation to explore the radiative impact of eruptions over a wide covarying range of SO2 emission magnitudes, injection heights, and eruption latitudes based on only 30 simulations. We use the emulators to build response surfaces to visualize and predict the sulfate aerosol e‐folding decay time, the stratospheric aerosol optical depth, and net radiative forcing of thousands of different eruptions. We find that the volcanic stratospheric aerosol optical depth and net radiative forcing are primarily determined by the mass of SO2 emitted, but eruption latitude is the most important parameter in determining the sulfate aerosol e‐folding decay time. The response surfaces reveal joint effects of the eruption source parameters in influencing the net radiative forcing, such as a stronger influence of injection height for tropical eruptions than high‐latitude eruptions. We also demonstrate how the emulated response surfaces can be used to find all combinations of eruption source parameters that produce a particular volcanic response, often revealing multiple solutions. Key Points: We demonstrate the feasibility and value of using statistical emulation to quantify the radiative impact of volcanic eruptionsEmulated response surfaces illustrate the dependencies of model output such as net radiative forcing on eruption source parametersEmulated response surfaces can also be used to constrain the eruption source parameters for a particular volcanic response [ABSTRACT FROM AUTHOR]

Stratospheric ozone loss inside the Arctic polar vortex for the winters between 2004–2005 and 2012–2013 has been quantified using measurements from the space-borne Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). For the first time, an evaluation has been performed of six different ozone loss estimation methods based on the same single observational dataset to determine the Arctic ozone loss (mixing ratio loss profiles and the partial-column ozone losses between 380 and 550 K). The methods used are the tracer-tracer correlation, the artificial tracer correlation, the average vortex profile descent, and the passive subtraction with model output from both Lagrangian and Eulerian chemical transport models (CTMs). For the tracer-tracer, the artificial tracer, and the average vortex profile descent approaches, various tracers have been used that are also measured by ACE-FTS. From these seven tracers investigated (CH4 , N2O , HF , OCS , CFC-11, CFC-12, and CFC-113), we found that CH4 , N2O , HF , and CFC-12 are the most suitable tracers for investigating polar stratospheric ozone depletion with ACE-FTS v3.5. The ozone loss estimates (in terms of the mixing ratio as well as total column ozone) are generally in good agreement between the different methods and among the different tracers. However, using the average vortex profile descent technique typically leads to smaller maximum losses (by approximately 15–30 DU) compared to all other methods. The passive subtraction method using output from CTMs generally results in slightly larger losses compared to the techniques that use ACE-FTS measurements only. The ozone loss computed, using both measurements and models, shows the greatest loss during the 2010–2011 Arctic winter. For that year, our results show that maximum ozone loss (2.1–2.7 ppmv) occurred at 460 K. The estimated partial-column ozone loss inside the polar vortex (between 380 and 550 K) using the different methods is 66–103, 61–95, 59–96, 41–89, and 85–122 DU for March 2005, 2007, 2008, 2010, and 2011, respectively. Ozone loss is difficult to diagnose for the Arctic winters during 2005–2006, 2008–2009, 2011–2012, and 2012–2013, because strong polar vortex disturbance or major sudden stratospheric warming events significantly perturbed the polar vortex, thereby limiting the number of measurements available for the analysis of ozone loss. [ABSTRACT FROM AUTHOR]

An updated and improved version of a global, vertically resolved, monthly mean zonal mean ozone database has been calculated - hereafter referred to as the BSVertOzone (Bodeker Scientific Vertical Ozone) database. Like its predecessor, it combines measurements from several satellite-based instruments and ozone profile measurements from the global ozonesonde network. Monthly mean zonal mean ozone concentrations in mixing ratio and number density are provided in 5° latitude bins, spanning 70 altitude levels (1 to 70 km), or 70 pressure levels that are approximately 1 km apart (878.4 to 0.046 hPa). Different data sets or "tiers" are provided: Tier 0 is based only on the available measurements and therefore does not completely cover the whole globe or the full vertical range uniformly; the Tier 0.5 monthly mean zonal means are calculated as a filled version of the Tier 0 database where missing monthly mean zonal mean values are estimated from correlations against a total column ozone (TCO) database. The Tier 0.5 data set includes the full range of measurement variability and is created as an intermediate step for the calculation of the Tier 1 data where a least squares regression model is used to attribute variability to various known forcing factors for ozone. Regression model fit coefficients are expanded in Fourier series and Legendre polynomials (to account for seasonality and latitudinal structure, respectively). Four different combinations of contributions from selected regression model basis functions result in four different Tier 1 data sets that can be used for comparisons with chemistry-climate model (CCM) simulations that do not exhibit the same unforced variability as reality (unless they are nudged towards reanalyses). Compared to previous versions of the database, this update includes additional satellite data sources and ozonesonde measurements to extend the database period to 2016. Additional improvements over the previous version of the database include the following: (i) adjustments of measurements to account for biases and drifts between different data sources (using a chemistry-transport model, CTM, simulation as a transfer standard), (ii) a more objective way to determine the optimum number of Fourier and Legendre expansions for the basis function fit coefficients, and (iii) the derivation of methodological and measurement uncertainties on each database value are traced through all data modification steps. Comparisons with the ozone database from SWOOSH (Stratospheric Water and OzOne Satellite Homogenized data set) show good agreement in many regions of the globe. Minor differences are caused by different bias adjustment procedures for the two databases. [ABSTRACT FROM AUTHOR]

Despite the recently reported beginning of a recovery in global stratospheric ozone (O3), an unexpected O3 decline in the tropical mid-stratosphere (around 30-35 km altitude) was observed in satellite measurements during the first decade of the 21st century. We use SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) measurements for the period 2004-2012 to confirm the significant O3 decline. The SCIAMACHY observations also show that the decrease in O3 is accompanied by an increase in NO2. To reveal the causes of these observed O3 and NO2 changes, we performed simulations with the TOMCAT 3D Chemistry-Transport Model (CTM) using different chemical and dynamical forcings. For the 2004-2012 time period, the TOMCAT simulations reproduce the SCIAMACHY-observed O3 decrease and NO2 increase in the tropical mid-stratosphere. The simulations suggest that the positive changes in NO2 (around 7% per decade) are due to similar positive changes in reactive odd nitrogen (NOy), which are a result of a longer residence time of the source gas N2O and increased production via N2O + O(¹D). The model simulations show a negative change of 10% per decade in N2O that is most likely due to variations in the deep branch of the Brewer-Dobson Circulation (BDC). Interestingly, modelled annual mean age-of-air (AoA) does not show any significant changes in the transport in the tropical mid-stratosphere during 2004-2012. However, further analysis of model results demonstrate significant seasonal variations. During the autumn months (September-October) there are positive AoA changes, that imply transport slowdown and a longer residence time of N2O allowing larger conversion to NOy which enhances O3 loss. During winter months (January-February) there are negative AoA changes, indicating faster N2O transport and less NOy production. Although the changes in AoA cancel out when averaging over the year, non-linearities in the chemistry-transport interactions mean that the net negative N2O change remains. [ABSTRACT FROM AUTHOR]

The Stratospheric Sulfur and its Role in Climate (SSiRC) Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP) explores uncertainties in the processes that connect volcanic emission of sulfur gas species and the radiative forcing associated with the resulting enhancement of the stratospheric aerosol layer. The central aim of ISA-MIP is to constrain and improve interactive stratospheric aerosol models and reduce uncertainties in the stratospheric aerosol forcing by comparing results of standardized model experiments with a range of observations. In this paper we present four co-ordinated inter-model experiments designed to investigate key processes which influence the formation and temporal development of stratospheric aerosol in different time periods of the observational record. The Background (BG) experiment will focus on microphysics and transport processes under volcanically quiescent conditions, when the stratospheric aerosol is controlled by the transport of aerosols and their precursors from the troposphere to the stratosphere. The Transient Aerosol Record (TAR) experiment will explore the role of small- to moderate-magnitude volcanic eruptions, anthropogenic sulfur emissions, and transport processes over the period 1998-2012 and their role in the warming hiatus. Two further experiments will investigate the stratospheric sulfate aerosol evolution after major volcanic eruptions. The Historical Eruptions SO2 Emission Assessment (HErSEA) experiment will focus on the uncertainty in the initial emission of recent large-magnitude volcanic eruptions, while the Pinatubo Emulation in Multiple models (PoEMS) experiment will provide a comprehensive uncertainty analysis of the radiative forcing from the 1991 Mt Pinatubo eruption. [ABSTRACT FROM AUTHOR]

We analyse simulations performed for the Chemistry-Climate Model Initiative (CCMI) to estimate the return dates of the stratospheric ozone layer from depletion caused by anthropogenic stratospheric chlorine and bromine. We consider a total of 155 simulations from 20 models, including a range of sensitivity studies which examine the impact of climate change on ozone recovery. For the control simulations (unconstrained by nudging towards analysed meteorology) there is a large spread (±20 DU in the global average) in the predictions of the absolute ozone column. Therefore, the model results need to be adjusted for biases against historical data. Also, the interannual variability in the model results need to be smoothed in order to provide a reasonably narrow estimate of the range of ozone return dates. Consistent with previous studies, but here for a Representative Concentration Pathway (RCP) of 6.0, these new CCMI simulations project that global total column ozone will return to 1980 values in 2049 (with a 1σ uncertainty of 2043–2055). At Southern Hemisphere mid-latitudes column ozone is projected to return to 1980 values in 2045 (2039–2050), and at Northern Hemisphere mid-latitudes in 2032 (2020–2044). In the polar regions, the return dates are 2060 (2055–2066) in the Antarctic in October and 2034 (2025–2043) in the Arctic in March. The earlier return dates in the Northern Hemisphere reflect the larger sensitivity to dynamical changes. Our estimates of return dates are later than those presented in the 2014 Ozone Assessment by approximately 5–17 years, depending on the region, with the previous best estimates often falling outside of our uncertainty range. In the tropics only around half the models predict a return of ozone to 1980 values, around 2040, while the other half do not reach the 1980 value. All models show a negative trend in tropical total column ozone towards the end of the 21st century. The CCMI models generally agree in their simulation of the time evolution of stratospheric chlorine and bromine, which are the main drivers of ozone loss and recovery. However, there are a few outliers which show that the multi-model mean results for ozone recovery are not as tightly constrained as possible. Throughout the stratosphere the spread of ozone return dates to 1980 values between models tends to correlate with the spread of the return of inorganic chlorine to 1980 values. In the upper stratosphere, greenhouse gas-induced cooling speeds up the return by about 10–20 years. In the lower stratosphere, and for the column, there is a more direct link in the timing of the return dates of ozone and chlorine, especially for the large Antarctic depletion. Comparisons of total column ozone between the models is affected by different predictions of the evolution of tropospheric ozone within the same scenario, presumably due to differing treatment of tropospheric chemistry. Therefore, for many scenarios, clear conclusions can only be drawn for stratospheric ozone columns rather than the total column. As noted by previous studies, the timing of ozone recovery is affected by the evolution of N2O and CH4. However, quantifying the effect in the simulations analysed here is limited by the few realisations available for these experiments compared to internal model variability. The large increase in N2O given in RCP 6.0 extends the ozone return globally by ∼ 15 years relative to N2O fixed at 1960 abundances, mainly because it allows tropical column ozone to be depleted. The effect in extratropical latitudes is much smaller. The large increase in CH4 given in the RCP 8.5 scenario compared to RCP 6.0 also lengthens ozone return by ∼ 15 years, again mainly through its impact in the tropics. Overall, our estimates of ozone return dates are uncertain due to both uncertainties in future scenarios, in particular those of greenhouse gases, and uncertainties in models. The scenario uncertainty is small in the short term but increases with time, and becomes large by the end of the century. There are still some model–model differences related to well-known processes which affect ozone recovery. Efforts need to continue to ensure that models used for assessment purposes accurately represent stratospheric chemistry and the prescribed scenarios of ozone-depleting substances, and only those models are used to calculate return dates. For future assessments of single forcing or combined effects of CO2, CH4, and N2O on the stratospheric column ozone return dates, this work suggests that it is more important to have multi-member (at least three) ensembles for each scenario from every established participating model, rather than a large number of individual models. [ABSTRACT FROM AUTHOR]