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Past studies have suggested that ozone in the troposphere has increased globally throughout much of the 20th century due to increases in anthropogenic emissions and transport. We show, by combining satellite measurements with a chemical transport model, that during the last four decades tropospheric ozone does indeed indicate increases that are global in nature, yet still highly regional. Satellite ozone measurements from Nimbus-7 and Earth Probe Total Ozone Mapping Spectrometer (TOMS) are merged with ozone measurements from the Aura Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) to determine trends in tropospheric ozone for 1979–2016. Both TOMS (1979–2005) and OMI/MLS (2005–2016) depict large increases in tropospheric ozone from the Near East to India and East Asia and further eastward over the Pacific Ocean. The 38-year merged satellite record shows total net change over this region of about +6 to +7 Dobson units (DU) (i.e., ∼15 %–20 % of average background ozone), with the largest increase (∼4 DU) occurring during the 2005–2016 Aura period. The Global Modeling Initiative (GMI) chemical transport model with time-varying emissions is used to aid in the interpretation of tropospheric ozone trends for 1980–2016. The GMI simulation for the combined record also depicts the greatest increases of +6 to +7 DU over India and East Asia, very similar to the satellite measurements. In regions of significant increases in tropospheric column ozone (TCO) the trends are a factor of 2–2.5 larger for the Aura record when compared to the earlier TOMS record; for India and East Asia the trends in TCO for both GMI and satellite measurements are ∼+3 DU decade(exp −1) or greater during 2005–2016 compared to about +1.2 to +1.4 DU decade(exp −1) for 1979–2005. The GMI simulation and satellite data also reveal a tropospheric ozone increases in ∼+4 to +5 DU for the 38-year record over central Africa and the tropical Atlantic Ocean. Both the GMI simulation and satellite-measured tropospheric ozone during the latter Aura time period show increases of ∼+3 DU decade−1 over the N Atlantic and NE Pacific.

The Sahara is the world's largest source of aeolian desert dust, but precise information on specific sources of this material is poor and sometimes contradictory. This paper uses daily data from the Total Ozone Mapping Spectrometer (TOMS) for 1999 to identify source areas for major dust events and their trajectories of long-range transport. Two major source areas are identified: the Bodélé depression and an area covering eastern Mauritania, western Mali and southern Algeria. Both of these major dust sources are primarily driven by natural factors since they are little affected by anthropogenic activities.

The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of −2.38 % and −2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO2 from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4.

The ozone data record from the Ozone Monitoring Instrument (OMI) onboard the NASA Earth Observing System (EOS) Aura satellite has proven to be very stable over the 10-plus years of operation. The OMI total column ozone processed through the Total Ozone Mapping Spectrometer (TOMS) ozone retrieval algorithm (version 8.5) has been compared with ground-based measurements and with ozone from a series of SBUV/2 (Solar Backscatter Ultraviolet) instruments. Comparison with an ensemble of Brewer–Dobson sites shows an absolute offset of about 1.5 % and almost no relative trend. Comparison with a merged ozone data set (MOD) created by combining data from a series of SBUV/2 instruments again shows an offset, of about 1 %, and a relative trend of less than 0.5 % over 10 years. The offset is mostly due to the use of the old Bass–Paur ozone cross sections in the OMI retrievals rather than the Brion–Daumont–Malicet cross sections that are now recommended. The bias in the Southern Hemisphere is smaller than that in the Northern Hemisphere, 0.9 % vs. 1.5 %, for reasons that are not completely understood. When OMI was compared with the European realization of a multi-instrument ozone time series, the GTO (GOME type Total Ozone) data set, there was a small trend of about −0.85 % decade−1. Since all the comparisons of OMI relative to other ozone measuring systems show relative trends that are less than 1 % decade−1, we conclude that the OMI total column ozone data are sufficiently stable that they can be used in studies of ozone trends.

On board the Deep Space Climate Observatory (DSCOVR), the first Earth-observing satellite at the L1 point (the first Lagrangian point in the Earth–Sun system), the Earth Polychromatic Imaging Camera (EPIC) continuously observes the entire sunlit face of the Earth. EPIC measures the solar backscattered and reflected radiances in 10 discrete spectral channels, four of which are in the ultraviolet (UV) range. These UV bands are selected primarily for total ozone (O3) and aerosol retrievals based on heritage algorithms developed for the series of Total Ozone Mapping Spectrometers (TOMS). These UV measurements also provide sensitive detection of sulfur dioxide (SO2) and volcanic ash, both of which may be episodically injected into the atmosphere during explosive volcanic eruptions. This paper presents the theoretical basis and mathematical procedures for the direct vertical column fitting (DVCF) algorithm used for retrieving total vertical columns of O3 and SO2 from DSCOVR EPIC. This paper describes algorithm advances, including an improved O3 profile representation that enables profile adjustments from multiple spectral measurements and the spatial optimal estimation (SOE) scheme that reduces O3 artifacts resulting from EPIC's band-to-band misregistrations. Furthermore, this paper discusses detailed error analyses and presents intercomparisons with correlative data to validate O3 and SO2 retrievals from EPIC.

This paper describes a new discrete wavelength algorithm developed for retrieving volcanic sulfur dioxide (SO2) vertical column density (VCD) from UV observing satellites. The Multi-Satellite SO2 algorithm (MS_SO2) simultaneously retrieves column densities of sulfur dioxide, ozone, and Lambertian effective reflectivity (LER) and its spectral dependence. It is used operationally to process measurements from the heritage Total Ozone Mapping Spectrometer (TOMS) onboard NASA's Nimbus-7 satellite (N7/TOMS: 1978–1993) and from the current Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR: 2015–ongoing) from the Earth–Sun Lagrange (L1) orbit. Results from MS_SO2 algorithm for several volcanic cases were assessed using the more sensitive principal component analysis (PCA) algorithm. The PCA is an operational algorithm used by NASA to retrieve SO2 from hyperspectral UV spectrometers, such as the Ozone Monitoring Instrument (OMI) onboard NASA's Earth Observing System Aura satellite and Ozone Mapping and Profiling Suite (OMPS) onboard NASA–NOAA Suomi National Polar Partnership (SNPP) satellite. For this comparative study, the PCA algorithm was modified to use the discrete wavelengths of the Nimbus-7/TOMS instrument, described in Sect. S1 of the Supplement. Our results demonstrate good agreement between the two retrievals for the largest volcanic eruptions of the satellite era, such as the 1991 Pinatubo eruption. To estimate SO2 retrieval systematic uncertainties, we use radiative transfer simulations explicitly accounting for volcanic sulfate and ash aerosols. Our results suggest that the discrete-wavelength MS_SO2 algorithm, although less sensitive than hyperspectral PCA algorithm, can be adapted to retrieve volcanic SO2 VCDs from contemporary hyperspectral UV instruments, such as OMI and OMPS, to create consistent, multi-satellite, long-term volcanic SO2 climate data records.

The purpose of this work is to study Rossby wave parameters in total ozone over the Arctic in 2000–2021. We consider the averages in the January–March period, when stratospheric trace gases (including ozone) in sudden stratospheric warming events are strongly disturbed by planetary waves. To characterize the wave parameters, we analyzed ozone data at the latitudes of 50°N (the sub-vortex area), 60°N (the polar vortex edge) and 70°N (inner region of the polar vortex). Total ozone column (TOC) measurements over a 22-year time interval were used from the Total Ozone Mapping Spectrometer/Earth Probe and Ozone Mapping Instrument/Aura satellite observations. The TOC zonal distribution and variations in the Fourier spectral components with zonal wave numbers m = 1–5 are presented. The daily and interannual variations in TOC, amplitudes and phases of the spectral wave components, as well as linear trends in the amplitudes of the dominant quasi-stationary wave 1 (QSW1), are discussed. The positive TOC peaks inside the vortex in 2010 and 2018 alternate with negative ones in 2011 and 2020. The extremely low TOC at 70°N in 2020 corresponds to severe depletion of stratospheric ozone over the Arctic in strong vortex conditions due to anomalously low planetary wave activity and a high positive phase of the Arctic Oscillation. Interannual TOC variations in the sub-vortex region at 50°N are accompanied by a negative trend of −4.8 Dobson Units per decade in the QSW1 amplitude, statistically significant at 90% confidence level, while the trend is statistically insignificant in the vortex edge region and inside the vortex due to the increased variability in TOC and QSW1. The processes associated with quasi-circumpolar migration and quasi-stationary oscillation of the wave-1 phase depending on the polar vortex strength in 2020 and 2021 are discussed.

While the stratospheric ozone protects the biosphere against ultraviolet (UV) radiation, tropospheric ozone acts like a greenhouse gas and an indicator of anthropogenic pollution. In this paper, we combined ground-based and satellite ozone observations over Irene site (25.90◦ S, 28.22◦ E), one of the most ancient ozone-observing stations in the southern tropics. The dataset is made of daily total columns and weekly profiles of ozone collected over 20 years, from 1998 to 2017. In order to fill in some missing data and split the total column of ozone into a tropospheric and a stratospheric column, we used satellite observations from TOMS (Total Ozone Mapping Spectrometer), OMI (Ozone Monitoring Instrument), and MLS (Microwave Limb Sounder) experiments. The tropospheric column is derived by integrating ozone profiles from an ozonesonde experiment, while the stratospheric column is obtained by subtracting the tropospheric column from the total column (recorded by the Dobson spectrometer), and by assuming that the mesospheric contribution is negligible. Each of the obtained ozone time series was then analyzed by applying the method of wavelet transform, which permitted the determination of the main forcings that contribute to each ozone time series. We then applied the multivariate Trend-Run model and the Mann–Kendall test for trend analysis. Despite the different analytical approaches, the obtained results are broadly similar and consistent. They showed a decrease in the stratospheric column (−0.56% and −1.7% per decade, respectively, for Trend-Run and Mann–Kendall) and an increase in the tropospheric column (+2.37% and +3.6%, per decade, respectively, for Trend-Run and Mann–Kendall). Moreover, the results presented here indicated that the slowing down of the total ozone decline is somewhat due to the contribution of the tropospheric ozone concentration.

This paper presents 23 years (1992–2014) of quasi-continuous measurements of the total ozone column (TOC) over the Southern Space Observatory (SSO) in São Martinho da Serra, Brazil (29.26° S, 53.48° and 488 m altitude). The TOC was measured by a Brewer spectrometer, and the results are also compared to daily and monthly observations from the TOMS (Total Ozone Mapping Spectrometer) and OMI (Ozone Monitoring Instrument) satellite instruments. Analyses of the main interannual modes of variability computed using the wavelet transform method were performed. A favorable agreement between the Brewer spectrophotometer and satellite datasets was found. The seasonal TOC variation is dominated by an annual cycle, with a minimum of approximately 260 DU in April and a maximum of approximately 295 DU in September. The wavelet analysis applied in the SSO TOC anomaly time series revealed that the Quasi-Biennial Oscillation (QBO) modulation was the main mode of interannual variability. The comparison between the SSO TOC anomaly time series with the QBO index revealed that the two are in opposite phases.

Daily total column ozone (TCO) measured using the Pandora spectrophotometer (no. 19) was compared with data from the Dobson (no. 124) and Brewer (no. 148) spectrophotometers, as well as from the Ozone Monitoring Instrument (OMI) (with two different algorithms, Total Ozone Mapping Spectrometer (TOMS) TOMS and differential optical absorption spectroscopy (DOAS) methods), over the 2-year period between March 2012 and March 2014 at Yonsei University, Seoul, Korea. Based on the linear-regression method, the TCO from Pandora is closely correlated with those from other instruments with regression coefficients (slopes) of 0.95 (Dobson), 1.00 (Brewer), 0.98 (OMI-TOMS), and 0.97 (OMI-DOAS), and determination coefficients (R2) of 0.95 (Dobson), 0.97 (Brewer), 0.96 (OMI-TOMS), and 0.95 (OMI-DOAS). The daily averaged TCO from Pandora has within 3 % differences compared to TCO values from other instruments. For the Dobson measurements in particular, the difference caused by the inconsistency in observation times when compared with the Pandora measurements was up to 12.5 % because of diurnal variations in the TCO values. However, the comparison with Brewer after matching the observation time shows agreement with large R2 and small biases. The TCO ratio between Brewer and Pandora shows the 0.98 ± 0.03, and the distributions for relative differences between two instruments are 89.2 and 57.1 % of the total data within the error ranges of 3 and 5 %, respectively. The TCO ratio between Brewer and Pandora also is partially dependent on solar zenith angle. The error dependence by the observation geometry is essential to the further analysis focusing on the sensitivity of aerosol and the stray-light effect in the instruments.