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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]

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]

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]

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]

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 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 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]