Search

Since the launch of TROPOMI on the Sentinel-5 Precursor (S5P) satellite, NO2 observations have become available with a resolution of 3.5× 5 km, which makes monitoring NOx emissions possible at the scale of city districts and industrial facilities. For Europe, emissions are reported on an annual basis for country totals and large industrial facilities and made publicly available via the European Environment Agency (EEA). Satellite observations can provide independent and more timely information on NOx emissions. A new version of the inversion algorithm DECSO (Daily Emissions Constrained by Satellite Observations) has been developed for deriving emissions for Europe on a daily basis, averaged to monthly mean maps. The estimated precision of these monthly emissions is about 25 % for individual grid cells. These satellite-derived emissions from DECSO have been compared to the officially reported European emissions and spatial–temporal disaggregated emission inventories. The country total DECSO NOx emissions are close to the reported emissions and the emissions compiled by the Copernicus Atmosphere Monitoring Service (CAMS). Comparison of the spatially distributed NOx emissions of DECSO and CAMS showed that the satellite-derived emissions are often higher in cities, while they are similar for large power plants and slightly lower in rural areas.

Urban heat island (UHI) and urban air pollution are two major environmental problems faced by many metropolises. The UHI affects air pollution by changing the local circulation and the chemical reaction environment, e.g. air temperature and relative humidity. In this study, the WRF-CMAQ (Weather Research and Forecasting Community Multiscale Air Quality) model was used to investigate the impact of an UHI on the vertical distribution of aerosol particles, especially secondary inorganic aerosol (SIA), taking the strong UHI in Hangzhou, China, as an example. The results show that during the day with the strongest UHI case of the 8 d simulation, the UHI effect resulted in a decrease in the PM2.5 concentrations in the boundary layer (BL) by about 33 %, accompanied by an increase in the lower free troposphere (LFT) by about 19 %. This is mostly attributed to the UHI circulation (UHIC) effect, which accounted for 91 % of the UHI-induced variations in PM2.5, rather than the UHI temperature or humidity effects, which contributed only 5 % and 4 %, respectively. The UHIC effect plays a dominant role, ranging from 72 % to 93 %, in UHI-induced PM2.5 variation in all eight UHI cases. The UHIC not only directly transports aerosol particles from ground level to the LFT but also redistributes aerosol precursors. During the strongest UHI case, about 80 % of the UHIC-induced increase in the aerosol particles in the LFT is due to direct transport of aerosol particles, whereas the other 20 % is due to secondary aerosol formation resulting from the transport of aerosol precursor gases. Of this 20 %, 91 % is contributed by SIA, especially ammonium nitrate aerosol formed from ammonia and nitric acid. In the atmosphere, ammonium nitrate is in equilibrium with ammonia and nitric acid, and the equilibrium depends on the ambient temperature. In the lower urban BL, the temperature is higher than in the LFT, and the ammonium nitrate equilibrium in the lower BL is more toward the gas phase than in the LFT; when these gases are transported by the UHIC into the colder LFT, the equilibrium shifts to the aerosol phase. Hence, the UHIC changes the vertical distribution of SIA, which may have potential implications on the radiation budget, cloud formation, and precipitation in the urban and surrounding areas.

Ozone (O3) plays a significant role in weather and climate on regional to global spatial scales. Most studies on the variability in the total column of O3 (TCO) are typically carried out using daytime data. Based on knowledge of the chemistry and transport of O3, significant deviations between daytime and night-time O3 are only expected either in the planetary boundary layer (PBL) or high in the stratosphere or mesosphere, with little effect on the TCO. Hence, we expect the daytime and night-time TCO to be very similar. However, a detailed evaluation of satellite measurements of daytime and night-time TCO is still lacking, despite the existence of long-term records of both. Thus, comparing daytime and night-time TCOs provides a novel approach to verifying the retrieval algorithms of instruments such as the Atmospheric Infrared Sounder (AIRS) and the Microwave Limb Sounder (MLS). In addition, such a comparison also helps to assess the value of night-time TCO for scientific research. Applying this verification on the AIRS and the MLS data, we identified inconsistencies in observations of O3 from both satellite instruments. For AIRS, daytime–night-time differences were found over oceans resembling cloud cover patterns and over land, mostly over dry land areas, which is likely related to infrared surface emissivity. These differences point to issues with the representation of both processes in the AIRS retrieval algorithm. For MLS, a major issue was identified with the “ascending–descending” orbit flag, used to discriminate night-time and daytime MLS measurements. Disregarding this issue, MLS day–night differences were significantly smaller than AIRS day–night differences, providing additional support for the retrieval method origin of AIRS in stratospheric column ozone (SCO) day–night differences. MLS day–night differences are dominated by the upper-stratospheric and mesospheric diurnal O3 cycle. These results provide useful information for improving infrared O3 products.

We derived global tropospheric ozone (O3) columns from GOME-2A (Global Ozone Monitoring Experiment) and OMI (Ozone Monitoring Instrument) O3 profiles, which were simultaneously assimilated into the TM5 (Tracer Model, version 5) global chemistry transport model for the year 2008. The horizontal model resolution has been increased by a factor of 6 for more accurate results. To reduce computational cost, the number of model layers has been reduced from 44 to 31. The model ozone fields are used to derive tropospheric ozone, which is defined here as the partial column between mean sea level and 6 km altitude. Two methods for calculating the tropospheric columns from the free model run and assimilated O3 fields are compared. In the first method, we calculate the residual between assimilated total columns and the partial model column between 6 km and the top of atmosphere. In the second method, we perform a direct integration of the assimilated O3 fields between the surface and 6 km. The results are validated against tropospheric columns derived from ozone sonde measurements. Our results show that the residual method has too large a variation to be used reliably for the determination of tropospheric ozone, so the direct integration method has been used instead. The median global bias is smaller for the assimilated O3 fields than for the free model run, but the large variation makes it difficult to make definitive statements on a regional or local scale. The monthly mean ozone fields show significant improvements and more detail when comparing the assimilated O3 fields with the free model run, especially for features such as biomass-burning-enhanced O3 concentrations and outflow of O3 rich air from Asia over the Pacific.

In this work, a ∼21-year global dataset from four different satellite sensors with a mid-morning overpass (GOME/ERS-2, SCIAMACHY/ENVISAT, GOME-2/Metop-A, and GOME-2/Metop-B) is compiled to study the long-term tropospheric NO2 patterns and trends. The Global Ozone Monitoring Experiment (GOME) and GOME-2 data are “corrected” relative to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data to produce a self-consistent dataset that covers the period April 1996–September 2017. The highest tropospheric NO2 concentrations are seen over urban, industrialized, and highly populated areas and over ship tracks in the oceans. Tropospheric NO2 has generally decreased during the last 2 decades over the industrialized and highly populated regions of the western world (a total decrease of the order of ∼49 % over the US, the Netherlands, and the UK; ∼36 % over Italy and Japan; and ∼32 % over Germany and France) and increased over developing regions (a total increase of ∼160 % over China and ∼33 % over India). It is suggested here that linear trends cannot be used efficiently worldwide for such long periods. Tropospheric NO2 is very sensitive to socioeconomic changes (e.g., environmental protection policies, economic recession, warfare, etc.) which may cause either short-term changes or even a reversal of the trends. The application of a method capable of detecting the year when a reversal of trends happened shows that tropospheric NO2 concentrations switched from positive to negative trends and vice versa over several regions around the globe. A country-level analysis revealed clusters of countries that exhibit similar positive-to-negative or negative-to-positive trend reversals, while 29 out of a total of 64 examined megacities and large urban agglomerations experienced a trend reversal at some point within the last 2 decades.

Global observations of tropospheric nitrogen dioxide (NO2) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned activities at the international level. Harmonised, multi-decadal records of NO2 columns and their associated uncertainties can provide crucial information on how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best-practice NO2 retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO2, a 1°  ×  1° TM5-MP data assimilation scheme to estimate the stratospheric background and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO2 algorithm to the most current level-1 data sets to produce a complete 22-year data record that includes GOME (1995–2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO2 spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2 columns amount to typically 40 % over polluted scenes. The first validation results of the QA4ECV OMI NO2 columns and their uncertainties over Tai'an, China, in June 2006 suggest a small bias (−2 %) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO2 long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale.

Understanding long-term variations in aerosol loading is essential for evaluating the health and climate effects of airborne particulates as well as the effectiveness of pollution control policies. The expected satellite lifetime is about 10 to 15 years. Therefore, to study the variations of atmospheric constituents over longer periods information from different satellites must be utilized.Here we introduce a method to construct a combined annual and seasonal long time series of AOD at 550 nm using the Along-Track Scanning Radiometers (ATSR: ATSR-2 and AATSR combined) and the MODerate resolution Imaging Spectroradiometer on Terra (MODIS/Terra), which together cover the 1995–2017 period. The long-term (1995–2017) combined AOD time series are presented for all of mainland China, for southeastern (SE) China and for 10 selected regions in China. Linear regression was applied to the combined AOD time series constructed for individual L3 (1°  ×  1°) pixels to estimate the AOD tendencies for two periods: 1995–2006 (P1) and 2011–2017 (P2), with respect to the changes in the emission reduction policies in China.During P1, the annually averaged AOD increased by 0.006 (or 2 % of the AOD averaged over the corresponding period) per year across all of mainland China, reflecting increasing emissions due to rapid economic development. In SE China, the annual AOD positive tendency in 1995–2006 was 0.014 (3 %) per year, reaching maxima (0.020, or 4 %, per year) in Shanghai and the Pearl River Delta regions. After 2011, during P2, AOD tendencies reversed across most of China with the annually averaged AOD decreasing by −0.015 (−6 %) per year in response to the effective reduction of the anthropogenic emissions of primary aerosols, SO2 and NOx. The strongest AOD decreases were observed in the Chengdu (−0.045, or −8 %, per year) and Zhengzhou (−0.046, or −9 %, per year) areas, while over the North China Plain and coastal areas the AOD decrease was lower than −0.03 (approximately −6 %) per year. In the less populated areas the AOD decrease was small.The AOD tendency varied by both season and region. The increase in the annually averaged AOD during P1 was mainly due to an increase in summer and autumn in SE China (0.020, or 4 %, and 0.016, or 4 %, per year, respectively), while during winter and spring the AOD actually decreased over most of China. The AOD negative tendencies during the 2011–2017 period were larger in summer than in other seasons over the whole of China (ca. −0.021, or −7 %, per year) and over SE China (ca. −0.048, or −9 %, per year).The long-term AOD variations presented here show a gradual decrease in the AOD after 2011 with an average reduction of 30 %–50 % between 2011 and 2017. The effect is more visible in the highly populated and industrialized regions in SE China, as expected.

Aerosol optical depth (AOD) patterns and interannual and seasonal variations over China are discussed based on the AOD retrieved from the Along-Track Scanning Radiometer (ATSR-2, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite (2000–2017). The AOD products used were the ATSR Dual View (ADV) v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and deep blue (DB) AOD product. Together these datasets provide an AOD time series for 23 years, from 1995 to 2017. The difference between the AOD values retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and monthly aggregate comparisons as well as validation results. This allows for the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset correction.ADV and MODIS AOD validation results show similar high correlations with the Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while the corresponding bias is positive for MODIS (0.06) and negative for ADV (−0.07). Validation of the AOD products in similar conditions, when ATSR and MODIS/Terra overpasses are within 90 min of each other and when both ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs better than MODIS in autumn, while MODIS performs slightly better in spring and summer. In winter, both ADV and MODIS underestimate the AERONET AOD.Similar AOD patterns are observed by ADV and MODIS in annual and seasonal aggregates as well as in time series. ADV–MODIS difference maps show that MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north.The agreement between sensors regarding year-to-year AOD changes is quite good. During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between 2006 and 2011 AOD did not change much, showing minor minima in 2008–2009. From 2011 onward AOD decreased in the SE of China. Similar patterns exist in year-to-year ADV and MODIS annual AOD tendencies in the overlapping period. However, regional differences between the ATSR and MODIS AODs are quite large. The consistency between ATSR and MODIS with regards to the AOD tendencies in the overlapping period is rather strong in summer, autumn and overall for the yearly average; however, in winter and spring, when there is a difference in coverage between the two instruments, the agreement between ATSR and MODIS is lower.AOD tendencies in China during the 1995–2017 period will be discussed in more detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and MODIS is introduced, and combined AOD time series are analyzed.

Atmospheric ozone plays a key role in air quality and the radiation budget of the Earth, both directly and through its chemical influence on other trace gases. Assessments of the atmospheric ozone distribution and associated climate change therefore demand accurate vertically resolved ozone observations with both stratospheric and tropospheric sensitivity, on both global and regional scales, and both in the long term and at shorter timescales. Such observations have been acquired by two series of European nadir-viewing ozone profilers, namely the scattered-light UV–visible spectrometers of the GOME family, launched regularly since 1995 (GOME, SCIAMACHY, OMI, GOME-2A/B, TROPOMI, and the upcoming Sentinel-5 series), and the thermal infrared emission sounders of the IASI type, launched regularly since 2006 (IASI on Metop platforms and the upcoming IASI-NG on Metop-SG). In particular, several Level-2 retrieved, Level-3 monthly gridded, and Level-4 assimilated nadir ozone profile data products have been improved and harmonized in the context of the ozone project of the European Space Agency's Climate Change Initiative (ESA Ozone_cci). To verify their fitness for purpose, these ozone datasets must undergo a comprehensive quality assessment (QA), including (a) detailed identification of their geographical, vertical, and temporal domains of validity; (b) quantification of their potential bias, noise, and drift and their dependences on major influence quantities; and (c) assessment of the mutual consistency of data from different sounders. For this purpose we have applied to the Ozone_cci Climate Research Data Package (CRDP) released in 2017 the versatile QA and validation system Multi-TASTE, which has been developed in the context of several heritage projects (ESA's Multi-TASTE, EUMETSAT's O3M-SAF, and the European Commission's FP6 GEOmon and FP7 QA4ECV). This work, as the second in a series of four Ozone_cci validation papers, reports for the first time on data content studies, information content studies and ground-based validation for both the GOME- and IASI-type climate data records combined. The ground-based reference measurements have been provided by the Network for the Detection of Atmospheric Composition Change (NDACC), NASA's Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other ozonesonde and lidar stations contributing to the World Meteorological Organisation's Global Atmosphere Watch (WMO GAW). The nadir ozone profile CRDP quality assessment reveals that all nadir ozone profile products under study fulfil the GCOS user requirements in terms of observation frequency and horizontal and vertical resolution. Yet all L2 observations also show sensitivity outliers in the UTLS and are strongly correlated vertically due to substantial averaging kernel fluctuations that extend far beyond the kernel's 15 km FWHM. The CRDP typically does not comply with the GCOS user requirements in terms of total uncertainty and decadal drift, except for the UV–visible L4 dataset. The drift values of the L2 GOME and OMI, the L3 IASI, and the L4 assimilated products are found to be overall insignificant, however, and applying appropriate altitude-dependent bias and drift corrections make the data fit for climate and atmospheric composition monitoring and modelling purposes. Dependence of the Ozone_cci data quality on major influence quantities – resulting in data screening suggestions to users – and perspectives for the Copernicus Sentinel missions are additionally discussed.

In this study, the tropospheric NO2 vertical column density (VCD) over an urban site in Guangzhou megacity in China is investigated by means of MAX-DOAS measurements during a campaign from late March 2015 to mid-March 2016. A MAX-DOAS system was deployed at the Guangzhou Institute of Geochemistry of the Chinese Academy of Sciences and operated there for about 1 year, during the spring and summer months. The tropospheric NO2 VCDs retrieved by the MAX-DOAS are presented and compared with space-borne observations from GOME-2/MetOp-A, GOME-2/MetOp-B and OMI/Aura satellite sensors. The comparisons reveal good agreement between satellite and MAX-DOAS observations over Guangzhou, with correlation coefficients ranging between 0.795 for GOME-2B and 0.996 for OMI. However, the tropospheric NO2 loadings are underestimated by the satellite sensors on average by 25.1, 10.3 and 5.7 %, respectively, for OMI, GOME-2A and GOME-2B. Our results indicate that GOME-2B retrievals are closer to those of the MAX-DOAS instrument due to the lower tropospheric NO2 concentrations during the days with valid GOME-2B observations. In addition, the effect of the main coincidence criteria is investigated, namely the cloud fraction (CF), the distance (d) between the satellite pixel center and the ground-based measurement site, as well as the time period within which the MAX-DOAS data are averaged around the satellite overpass time. The effect of CF and time window criteria is more profound on the selection of OMI overpass data, probably due to its smaller pixel size. The available data pairs are reduced to half and about one-third for CF ≤ 0.3 and CF ≤ 0.2, respectively, while, compared to larger CF thresholds, the correlation coefficient is improved to 0.996 from about 0.86, the slope value is very close to unity ( ∼ 0.98) and the mean satellite underestimation is reduced to about half (from ∼ 7 to ∼ 3.5 × 1015 molecules cm−2). On the other hand, the distance criterion affects mostly GOME-2B data selection, because GOME-2B pixels are quite evenly distributed among the different radii used in the sensitivity test. More specifically, the number of collocations is notably reduced when stricter radius limits are applied, the r value is improved from 0.795 (d ≤ 50 km) to 0.953 (d ≤ 20 km), and the absolute mean bias decreases about 6 times for d ≤ 30 km compared to the reference case (d ≤ 50 km).

Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope = 1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of −30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

The main aim of this paper is to update existing sulfur dioxide (SO2) emission inventories over China using modern inversion techniques, state-of-the-art chemistry transport modelling (CTM) and satellite observations of SO2. Within the framework of the EU Seventh Framework Programme (FP7) MarcoPolo (Monitoring and Assessment of Regional air quality in China using space Observations) project, a new SO2 emission inventory over China was calculated using the CHIMERE v2013b CTM simulations, 10 years of Ozone Monitoring Instrument (OMI)/Aura total SO2 columns and the pre-existing Multi-resolution Emission Inventory for China (MEIC v1.2). It is shown that including satellite observations in the calculations increases the current bottom-up MEIC inventory emissions for the entire domain studied (15–55° N, 102–132° E) from 26.30 to 32.60 Tg annum−1, with positive updates which are stronger in winter ( ∼ 36 % increase). New source areas were identified in the southwest (25–35° N, 100–110° E) as well as in the northeast (40–50° N, 120–130° E) of the domain studied as high SO2 levels were observed by OMI, resulting in increased emissions in the a posteriori inventory that do not appear in the original MEIC v1.2 dataset. Comparisons with the independent Emissions Database for Global Atmospheric Research, EDGAR v4.3.1, show a satisfying agreement since the EDGAR 2010 bottom-up database provides 33.30 Tg annum−1 of SO2 emissions. When studying the entire OMI/Aura time period (2005 to 2015), it was shown that the SO2 emissions remain nearly constant before the year 2010, with a drift of −0.51 ± 0.38 Tg annum−1, and show a statistically significant decline after the year 2010 of −1.64 ± 0.37 Tg annum−1 for the entire domain. Similar findings were obtained when focusing on the greater Beijing area (30–40° N, 110–120° E) with pre-2010 drifts of −0.17 ± 0.14 and post-2010 drifts of −0.47 ± 0.12 Tg annum−1. The new SO2 emission inventory is publicly available and forms part of the official EU MarcoPolo emission inventory over China, which also includes updated NOx, volatile organic compounds and particulate matter emissions.

We present a 3-D climatology of the desert dust distribution over South and East Asia derived using CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) data. To distinguish desert dust from total aerosol load we apply a methodology developed in the framework of EARLINET (European Aerosol Research Lidar Network). The method involves the use of the particle linear depolarization ratio and updated lidar ratio values suitable for Asian dust, applied to multiyear CALIPSO observations (January 2007–December 2015). The resulting dust product provides information on the horizontal and vertical distribution of dust aerosols over South and East Asia along with the seasonal transition of dust transport pathways. Persistent high D_AOD (dust aerosol optical depth) values at 532 nm, of the order of 0.6, are present over the arid and semi-arid desert regions. Dust aerosol transport (range, height and intensity) is subject to high seasonality, with the highest values observed during spring for northern China (Taklimakan and Gobi deserts) and during summer over the Indian subcontinent (Thar Desert). Additionally, we decompose the CALIPSO AOD (aerosol optical depth) into dust and non-dust aerosol components to reveal the non-dust AOD over the highly industrialized and densely populated regions of South and East Asia, where the non-dust aerosols yield AOD values of the order of 0.5. Furthermore, the CALIPSO-based short-term AOD and D_AOD time series and trends between January 2007 and December 2015 are calculated over South and East Asia and over selected subregions. Positive trends are observed over northwest and east China and the Indian subcontinent, whereas over southeast China trends are mostly negative. The calculated AOD trends agree well with the trends derived from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), although significant differences are observed over specific regions.

A three-dimensional global ozone distribution has been derived from assimilation of ozone profiles that were observed by satellites. By simultaneous assimilation of ozone profiles retrieved from the nadir looking satellite instruments Global Ozone Monitoring Experiment 2 (GOME-2) and Ozone Monitoring Instrument (OMI), which measure the atmosphere at different times of the day, the quality of the derived atmospheric ozone field has been improved. The assimilation is using an extended Kalman filter in which chemical transport model TM5 has been used for the forecast. The combined assimilation of both GOME-2 and OMI improves upon the assimilation results of a single sensor. The new assimilation system has been demonstrated by processing 4 years of data from 2008 to 2011. Validation of the assimilation output by comparison with sondes shows that biases vary between −5 and +10 % between the surface and 100 hPa. The biases for the combined assimilation vary between −3 and +3 % in the region between 100 and 10 hPa where GOME-2 and OMI are most sensitive. This is a strong improvement compared to direct retrievals of ozone profiles from satellite observations.

Satellite nitrogen dioxide (NO2) observations have been widely used to evaluate emission changes. To determine trends in nitrogen oxides (NOx) emission over China, we used a method independent of chemical transport models to quantify the NOx emissions from 48 cities and seven power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO2 observations from 2005 to 2015. We found that NOx emissions over 48 Chinese cities increased by 52 % from 2005 to 2011 and decreased by 21 % from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e., power, industrial, and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NOx emissions was consistent with the bottom-up emission inventories for all power plants (r = 0. 8 on average), but not for some cities (r = 0. 4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to city level by using spatial distribution proxies.

We compare nine emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over mainland China, especially for trends, with an average bias of about 20 % for yearly emissions. All the inventories show the typical emission reduction of 10 % during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite-derived approach is that the emissions are soon available after observation, while the strength of the bottom-up inventories is that they include detailed information of emissions for each source category.

This study aims to cross-validate ground-based and satellite-based models of three photobiological UV effective dose products: the Commission Internationale de l'Éclairage (CIE) erythemal UV, the production of vitamin D in the skin, and DNA damage, using high-temporal-resolution surface-based measurements of solar UV spectral irradiances from a synergy of instruments and models. The satellite-based Tropospheric Emission Monitoring Internet Service (TEMIS; version 1.4) UV daily dose data products were evaluated over the period 2009 to 2014 with ground-based data from a Norsk Institutt for Luftforskning (NILU)-UV multifilter radiometer located at the northern midlatitude super-site of the Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (LAP/AUTh), in Greece. For the NILU-UV effective dose rates retrieval algorithm, a neural network (NN) was trained to learn the nonlinear functional relation between NILU-UV irradiances and collocated Brewer-based photobiological effective dose products. Then the algorithm was subjected to sensitivity analysis and validation. The correlation of the NN estimates with target outputs was high (r = 0. 988 to 0.990) and with a very low bias (0.000 to 0.011 in absolute units) proving the robustness of the NN algorithm. For further evaluation of the NILU NN-derived products, retrievals of the vitamin D and DNA-damage effective doses from a collocated Yankee Environmental Systems (YES) UVB-1 pyranometer were used. For cloud-free days, differences in the derived UV doses are better than 2 % for all UV dose products, revealing the reference quality of the ground-based UV doses at Thessaloniki from the NILU-UV NN retrievals. The TEMIS UV doses used in this study are derived from ozone measurements by the SCIAMACHY/Envisat and GOME2/MetOp-A satellite instruments, over the European domain in combination with SEVIRI/Meteosat-based diurnal cycle of the cloud cover fraction per 0. 5° × 0. 5° (lat × long) grid cells. TEMIS UV doses were found to be ∼ 12.5 % higher than the NILU NN estimates but, despite the presence of a visually apparent seasonal pattern, the R2 values were found to be robustly high and equal to 0.92–0.93 for 1588 all-sky coincidences. These results significantly improve when limiting the dataset to cloud-free days with differences of 0.57 % for the erythemal doses, 1.22 % for the vitamin D doses, and 1.18 % for the DNA-damage doses, with standard deviations of the order of 11–13 %. The improvement of the comparative statistics under cloud-free cases further testifies to the importance of the appropriate consideration of the contribution of clouds in the UV radiation reaching the Earth's surface. For the urban area of Thessaloniki, with highly variable aerosol, the weakness of the implicit aerosol information introduced to the TEMIS UV dose algorithm was revealed by comparison of the datasets to aerosol optical depths at 340 nm as reported by a collocated CIMEL sun photometer, operating in Thessaloniki at LAP/AUTh as part of the NASA Aerosol Robotic Network.

We improve the emission estimate algorithm DECSO (Daily Emission estimates Constrained by Satellite Observations) to better detect NOx emissions over remote areas. The new version is referred to as DECSO v5. The error covariance of the sensitivity of NO2 column observations to gridded NOx emissions has been better characterized. This reduces the background noise of emission estimates by a factor of 10. An emission update constraint has been added to avoid unrealistic day-to-day fluctuations of emissions. We estimate total NOx emissions, which include biogenic emissions that often drive the seasonal cycle of the NOx emissions. We demonstrate the improvements implemented in DECSO v5 for the domain of East Asia in the year 2012 and 2013. The emissions derived by DECSO v5 are in good agreement with other inventories like MIX. In addition, the improved algorithm is able to better capture the seasonality of NOx emissions and for the first time it reveals ship tracks near the Chinese coasts that are otherwise hidden by the outflow of NO2 from the Chinese mainland. The precision of monthly emissions derived by DECSO v5 for each grid cell is about 20 %.

Air quality observations by satellite instruments are global and have a regular temporal resolution, which makes them very useful in studying long-term trends in atmospheric species. To monitor air quality trends in China for the period 2005–2015, we derive SO2 columns and NOx emissions on a provincial level with improved accuracy. To put these trends into perspective they are compared with public data on energy consumption and the environmental policies of China. We distinguish the effect of air quality regulations from economic growth by comparing them relatively to fossil fuel consumption. Pollutant levels, per unit of fossil fuel, are used to assess the effectiveness of air quality regulations. We note that the desulfurization regulations enforced in 2005–2006 only had a significant effect in the years 2008–2009, when a much stricter control of the actual use of the installations began. For national NOx emissions a distinct decreasing trend is only visible from 2012 onwards, but the emission peak year differs from province to province. Unlike SO2, emissions of NOx are highly related to traffic. Furthermore, regulations for NOx emissions are partly decided on a provincial level. The last 3 years show a reduction both in SO2 and NOx emissions per fossil fuel unit, since the authorities have implemented several new environmental regulations. Despite an increasing fossil fuel consumption and a growing transport sector, the effects of air quality policy in China are clearly visible. Without the air quality regulations the concentration of SO2 would be about 2.5 times higher and the NO2 concentrations would be at least 25 % higher than they are today in China.

The main aim of the paper is to demonstrate an approach for post-processing of the Dobson spectrophotometers' total ozone columns (TOCs) in order to compensate for their known stratospheric effective temperature (Teff) dependency and its resulting effect on the usage of the Dobson TOCs for satellite TOCs' validation. The Dobson observations employed are those routinely submitted to the World Ozone and Ultraviolet Data Centre (WOUDC) of the World Meteorological Organization, whereas the effective temperatures have been extracted from two sources: the European Space Agency, ESA, Ozone Climate Change Initiative, Ozone-CCI, GODFIT version 3 (GOME-type Direct FITting) algorithm applied to the GOME2/MetopA, GOME2A, observations as well as the one derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) outputs. Both temperature sources are evaluated utilizing co-located ozonesonde measurements also retrieved from the WOUDC database. Both GODFIT_v3 and ECMWF Teffs are found to be unbiased against the ozonesonde observations and to agree with high correlation coefficients, especially for latitudes characterized by high seasonal variability in Teff. The validation analysis shows that, when applying the GODFIT_v3 effective temperatures in order to post-process the Dobson TOC, the mean difference between Dobson and GOME2A GODFIT_v3 TOCs moves from 0.63 ± 0.66 to 0.26 ± 0.46 % in the Northern Hemisphere and from 1.25 ± 1.20 to 0.80 ± 0.71 % in the Southern Hemisphere. The existing solar zenith angle dependency of the differences has been smoothed out, with near-zero dependency up to the 60–65° bin and the highest deviation decreasing from 2.38 ± 6.6 to 1.37 ± 6.4 % for the 80–85° bin. We conclude that the global-scale validation of satellite TOCs against collocated Dobson measurements benefits from a post-correction using suitably estimated Teffs.

The Nanjing Government applied temporary environmental regulations to guarantee good air quality during the Youth Olympic Games (YOG) in 2014. We study the effect of those regulations by applying the emission estimate algorithm DECSO (Daily Emission estimates Constrained by Satellite Observations) to measurements of the Ozone Monitoring Instrument (OMI). We improved DECSO by updating the chemical transport model CHIMERE from v2006 to v2013 and by adding an Observation minus Forecast (OmF) criterion to filter outlying satellite retrievals due to high aerosol concentrations. The comparison of model results with both ground and satellite observations indicates that CHIMERE v2013 is better performing than CHIMERE v2006. After filtering the satellite observations with high aerosol loads that were leading to large OmF values, unrealistic jumps in the emission estimates are removed. Despite the cloudy conditions during the YOG we could still see a decrease of tropospheric NO2 column concentrations of about 32 % in the OMI observations when compared to the average NO2 columns from 2005 to 2012. The results of the improved DECSO algorithm for NOx emissions show a reduction of at least 25 % during the YOG period and afterwards. This indicates that air quality regulations taken by the local government have an effect in reducing NOx emissions. The algorithm is also able to detect an emission reduction of 10 % during the Chinese Spring Festival. This study demonstrates the capacity of the DECSO algorithm to capture the change of NOx emissions on a monthly scale. We also show that the observed NO2 columns and the derived emissions show different patterns that provide complimentary information. For example, the Nanjing smog episode in December 2013 led to a strong increase in NO2 concentrations without an increase in NOx emissions. Furthermore, DECSO gives us important information on the non-trivial seasonal relation between NOx emissions and NO2 concentrations on a local scale.

The ozone multi-sensor reanalysis (MSR) is a multi-decadal ozone column data record constructed using all available ozone column satellite data sets, surface Brewer and Dobson observations and a data assimilation technique with detailed error modelling. The result is a high-resolution time series of 6-hourly global ozone column fields and forecast error fields that may be used for ozone trend analyses as well as detailed case studies. The ozone MSR is produced in two steps. First, the latest reprocessed versions of all available ozone column satellite data sets are collected and then are corrected for biases as a function of solar zenith angle (SZA), viewing zenith angle (VZA), time (trend), and stratospheric temperature using surface observations of the ozone column from Brewer and Dobson spectrophotometers from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Subsequently the de-biased satellite observations are assimilated within the ozone chemistry and data assimilation model TMDAM. The MSR2 (MSR version 2) reanalysis upgrade described in this paper consists of an ozone record for the 43-year period 1970–2012. The chemistry transport model and data assimilation system have been adapted to improve the resolution, error modelling and processing speed. Backscatter ultraviolet (BUV) satellite observations have been included for the period 1970–1977. The total record is extended by 13 years compared to the first version of the ozone multi sensor reanalysis, the MSR1. The latest total ozone retrievals of 15 satellite instruments are used: BUV-Nimbus4, TOMS-Nimbus7, TOMS-EP, SBUV-7, -9, -11, -14, -16, -17, -18, -19, GOME, SCIAMACHY, OMI and GOME-2. The resolution of the model runs, assimilation and output is increased from 2° × 3° to 1° × 1°. The analysis is driven by 3-hourly meteorology from the ERA-Interim reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) starting from 1979, and ERA-40 before that date. The chemistry parameterization has been updated. The performance of the MSR2 analysis is studied with the help of observation-minus-forecast (OmF) departures from the data assimilation, by comparisons with the individual station observations and with ozone sondes. The OmF statistics show that the mean bias of the MSR2 analyses is less than 1 % with respect to de-biased satellite observations after 1979.

A methodology for the round-robin evaluation and the geophysical validation of ozone profile data retrieved from nadir UV backscatter satellite measurements is detailed and discussed, consisting of data set content studies, information content studies, co-location studies, and comparisons with reference measurements. Within the European Space Agency's Climate Change Initiative on ozone (Ozone_cci project), the proposed round-robin procedure is applied to two nadir ozone profile data sets retrieved at the Royal Netherlands Meteorological Institute (KNMI) and the Rutherford Appleton Laboratory (RAL, United Kingdom), using their respective OPERA v1.26 and RAL v2.1 optimal estimation algorithms, from MetOp-A GOME-2 (i.e. the second generation Global Ozone Monitoring Experiment on the first Meteorological Operational Satellite) measurements taken in 2008. The ground-based comparisons use ozonesonde and lidar profiles as reference data, acquired by the Network for the Detection of Atmospheric Composition Change (NDACC), Southern Hemisphere Additional Ozonesonde programme (SHADOZ), and other stations of the World Meteorological Organisation's Global Atmosphere Watch (WMO GAW). This direct illustration highlights practical issues that inevitably emerge from discrepancies in e.g. profile representation and vertical smoothing, for which different recipes are investigated and discussed. Several approaches for information content quantification, vertical resolution estimation, and reference profile resampling are compared and applied as well. The paper concludes with compliance estimates of the two GOME-2 ozone profile data sets with user requirements from the Global Climate Observing System (GCOS) and from climate modellers.

This study presents a sensitivity analysis of multivariate regressions of recent springtime Antarctic vortex ozone trends using a "big data" ensemble approach. Our results indicate that the poleward heat flux (Eliassen–Palm flux) and the effective chlorine loading respectively explain most of the short-term and long-term variability in different Antarctic springtime total ozone records. The inclusion in the regression of stratospheric volcanic aerosols, solar variability and the quasi-biennial oscillation is shown to increase rather than decrease the overall uncertainty in the attribution of Antarctic springtime ozone because of large uncertainties in their respective records. Calculating the trend significance for the ozone record from the late 1990s onwards solely based on the fit of the effective chlorine loading is not recommended, as this does not take fit residuals into account, resulting in too narrow uncertainty intervals, while the fixed temporal change of the effective chlorine loading does not allow for any flexibility in the trends. When taking fit residuals into account in a piecewise linear trend fit, we find that approximately 30–60% of the regressions in the full ensemble result in a statistically significant positive springtime ozone trend over Antarctica from the late 1990s onwards. Analysis of choices and uncertainties in time series show that, depending on choices in time series and parameters, the fraction of statistically significant trends in parts of the ensemble can range from negligible to a complete 100% significance. We also find that, consistent with expectations, the number of statistically significant trends increases with increasing record length. Although our results indicate that the use multivariate regressions is a valid approach for assessing the state of Antarctic ozone hole recovery, and it can be expected that results will move towards more confidence in recovery with increasing record length, uncertainties in choices currently do not yet support formal identification of recovery of the Antarctic ozone hole.

Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979–2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009–2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño–Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively between 30° N and 30° S, particularly over the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid- to high latitudes. We observe ozone increases with potential vorticity and day length and ozone decreases with geopotential height and variable ozone effects due to the polar vortex in regions to the north and south of the polar vortices. Recovery of ozone is identified globally. However, recovery rates and uncertainties strongly depend on choices that can be made in defining the explanatory variables. The application of several trend models, each with their own pros and cons, yields a large range of recovery rate estimates. Overall these results suggest that care has to be taken in determining ozone recovery rates, in particular for the Antarctic ozone hole.

Peak stratospheric chlorofluorocarbon (CFC) and other ozone depleting substance (ODS) concentrations were reached in the mid- to late 1990s. Detection and attribution of the expected recovery of the stratospheric ozone layer in an atmosphere with reduced ODSs as well as efforts to understand the evolution of stratospheric ozone in the presence of increasing greenhouse gases are key current research topics. These require a critical examination of the ozone changes with an accurate knowledge of the spatial (geographical and vertical) and temporal ozone response. For such an examination, it is vital that the quality of the measurements used be as high as possible and measurement uncertainties well quantified. In preparation for the 2014 United Nations Environment Programme (UNEP)/World Meteorological Organization (WMO) Scientific Assessment of Ozone Depletion, the SPARC/IO3C/IGACO-O3/NDACC (SI2N) Initiative was designed to study and document changes in the global ozone profile distribution. This requires assessing long-term ozone profile data sets in regards to measurement stability and uncertainty characteristics. The ultimate goal is to establish suitability for estimating long-term ozone trends to contribute to ozone recovery studies. Some of the data sets have been improved as part of this initiative with updated versions now available. This summary presents an overview of stratospheric ozone profile measurement data sets (ground and satellite based) available for ozone recovery studies. Here we document measurement techniques, spatial and temporal coverage, vertical resolution, native units and measurement uncertainties. In addition, the latest data versions are briefly described (including data version updates as well as detailing multiple retrievals when available for a given satellite instrument). Archive location information for each data set is also given.

For the retrieval of the vertical distribution of ozone in the atmosphere the Ozone ProfilE Retrieval Algorithm (OPERA) has been further developed. The new version (1.26) of OPERA is capable of retrieving ozone profiles from UV–VIS observations of most nadir-looking satellite instruments like GOME, SCIAMACHY, OMI and GOME-2. The setup of OPERA is described and results are presented for GOME and GOME-2 observations. The retrieved ozone profiles are globally compared to ozone sondes for the years 1997 and 2008. Relative differences between GOME/GOME-2 and ozone sondes are within the limits as specified by the user requirements from the Climate Change Initiative (CCI) programme of ESA (20% in the troposphere, 15% in the stratosphere). To demonstrate the performance of the algorithm under extreme circumstances, the 2009 Antarctic ozone hole season was investigated in more detail using GOME-2 ozone profiles and lidar data, which showed an unusual persistence of the vortex over the Río Gallegos observing station (51° S, 69.3° W). By applying OPERA to multiple instruments, a time series of ozone profiles from 1996 to 2013 from a single robust algorithm can be created.

Due to changing economic activity, emissions of air pollutants in East Asia are changing rapidly in space and time. Monthly emission estimates of nitrogen oxides derived from satellite observations provide valuable insight into the evolution of anthropogenic activity on a regional scale. We present the first results of a new emission estimation algorithm, specifically designed to use daily satellite observations of column concentrations for fast updates of emissions of short-lived atmospheric constituents on a mesoscopic scale (~ 0.25° × 0.25°). The algorithm is used to construct a monthly NOx emission time series for the period 2007–2011 from tropospheric NO2 observations of GOME-2 for East Chinese provinces and surrounding countries. The new emission estimates correspond well with the bottom-up inventory of EDGAR v4.2, but are smaller than the inventories of INTEX-B and MEIC. They reveal a strong positive trend during 2007–2011 for almost all Chinese provinces, related to the country's economic development. We find a 41% increment of NOx emissions in East China during this period, which shows the need to update emission inventories in this region on a regular basis. Negative emission trends are found in Japan and South Korea, which can be attributed to a combined effect of local environmental policy and global economic crises. Analysis of seasonal variation distinguishes between regions with dominant anthropogenic or biogenic emissions. For regions with a mixed anthropogenic and biogenic signature, the opposite seasonality can be used for an estimation of the separate emission contributions. Finally, the non-local concentration/emission relationships calculated by the algorithm are used to quantify the direct effect of regional NOx emissions on tropospheric NO2 concentrations outside the region. For regions such as North Korea and the Beijing municipality, a substantial part of the tropospheric NO2 originates from emissions elsewhere.

Triggered by recent developments from laboratory and field studies regarding major NOx sink pathways in the troposphere, this study evaluates the influence of chemical uncertainties in NOx sinks for global NOx distributions calculated by the IMAGESv2 chemistry-transport model, and quantifies their significance for top-down NOx emission estimates. Our study focuses on five key chemical parameters believed to be of primary importance, more specifically, the rate of the reaction of NO2 with OH radicals, the newly identified HNO3-forming channel in the reaction of NO with HO2, the reactive uptake of N2O5 and HO2 by aerosols, and the regeneration of OH in the oxidation of isoprene. Sensitivity simulations are performed to estimate the impact of each source of uncertainty. The model calculations show that, although the NO2+OH reaction is the largest NOx sink globally accounting for ca. 60% of the total sink, the reactions contributing the most to the overall uncertainty are the formation of HNO3 in NO+HO2, leading to NOx column changes exceeding a factor of two over tropical regions, and the uptake of HO2 by aqueous aerosols, in particular over East and South Asia. Emission inversion experiments are carried out using model settings which either minimise (MINLOSS) or maximise (MAXLOSS) the total NOx sink, both constrained by one year of OMI NO2 column data from the DOMINO v2 KNMI algorithm. The choice of the model setup is found to have a major impact on the top-down flux estimates, with 75% higher emissions for MAXLOSS compared to the MINLOSS inversion globally. Even larger departures are found for soil NO (factor of 2) and lightning (1.8). The global anthropogenic source is better constrained (factor of 1.57) than the natural sources, except over South Asia where the combined uncertainty primarily associated to the NO+HO2 reaction in summer and HO2 uptake by aerosol in winter lead to top-down emission differences exceeding a factor of 2. Evaluation of the emission optimisation is performed against independent satellite observations from the SCIAMACHY sensor, with airborne NO2 measurements of the INTEX-A and INTEX-B campaigns, as well as with two new bottom-up inventories of anthropogenic emissions in Asia (REASv2) and China (MEIC). Neither the MINLOSS nor the MAXLOSS setup succeeds in providing the best possible match with all independent datasets. Whereas the minimum sink assumption leads to better agreement with aircraft NO2 profile measurements, consistent with the results of a previous analysis (Henderson et al., 2012), the same assumption leads to unrealistic features in the inferred distribution of emissions over China. Clearly, although our study addresses an important issue which was largely overlooked in previous inversion exercises, and demonstrates the strong influence of NOx loss uncertainties on top-down emission fluxes, additional processes need to be considered which could also influence the inferred source.

We present an improved tropospheric nitrogen dioxide column retrieval algorithm (DOMINO v2.0) for OMI based on better air mass factors (AMFs) and a correction for across-track stripes resulting from calibration errors in the OMI backscattered reflectances. Since October 2004, NO2 retrievals from the Ozone Monitoring Instrument (OMI), a UV/Vis nadir spectrometer onboard NASA's EOS-Aura satellite, have been used with success in several scientific studies focusing on air quality monitoring, detection of trends, and NOx emission estimates. Dedicated evaluations of previous DOMINO tropospheric NO2 retrievals indicated their good quality, but also suggested that the tropospheric columns were susceptible to high biases (by 0–40%), probably because of errors in the air mass factor calculations. Here we update the DOMINO air mass factor approach. We calculate a new look-up table (LUT) for altitude-dependent AMFs based on more realistic atmospheric profile parameters, and include more surface albedo and surface pressure reference points than before. We improve the sampling of the TM4 model, resulting in a priori NO2 profiles that are better mixed throughout the boundary layer. We evaluate the NO2 profiles simulated with the improved TM4 sampling as used in the AMF calculations and show that they are highly consistent with in situ NO2 measurements from aircraft during the INTEX-A and INTEX-B campaigns in 2004 and 2006. Our air mass factor calculations are further updated by the implementation of a high-resolution terrain height and a high-resolution surface albedo climatology based on OMI measurements. Together with a correction for across-track stripes, the overall impact of the improved terrain height and albedo descriptions is modest (2 simulated aloft, where sensitivity to NO2 is higher, and amount to reductions in tropospheric NO2 columns of up to 20% in winter, and 10% in summer over extended polluted areas. We investigate the impact of aerosols on the NO2 retrieval, and based on a comparison of concurrent retrievals of clouds from OMI and aerosols from MODIS Aqua, we find empirical evidence that OMI cloud retrievals are sensitive to the presence of scattering aerosols. It follows that an implicit correction for the effects of aerosols occurs through the aerosol-induced cloud parameters in DOMINO, and we show that such an empirical correction amounts to a 20 %AMF reduction in summer and ±10% changes in winter over the eastern United States.

A single coherent total ozone dataset, called the Multi Sensor Reanalysis (MSR), has been created from all available ozone column data measured by polar orbiting satellites in the near-ultraviolet Huggins band in the last thirty years. Fourteen total ozone satellite retrieval datasets from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A) have been used in the MSR. As first step a bias correction scheme is applied to all satellite observations, based on independent ground-based total ozone data from the World Ozone and Ultraviolet Data Center. The correction is a function of solar zenith angle, viewing angle, time (trend), and effective ozone temperature. As second step data assimilation was applied to create a global dataset of total ozone analyses. The data assimilation method is a sub-optimal implementation of the Kalman filter technique, and is based on a chemical transport model driven by ECMWF meteorological fields. The chemical transport model provides a detailed description of (stratospheric) transport and uses parameterisations for gas-phase and ozone hole chemistry. The MSR dataset results from a 30-year data assimilation run with the 14 corrected satellite datasets as input, and is available on a grid of 1× 1 1/2° with a sample frequency of 6 h for the complete time period (1978–2008). The Observation-minus-Analysis (OmA) statistics show that the bias of the MSR analyses is less than 1% with an RMS standard deviation of about 2% as compared to the corrected satellite observations used.

The Ozone Profile Algorithm (OPERA), developed at KNMI, retrieves the vertical ozone distribution from nadir spectral satellite measurements of back scattered sunlight in the ultraviolet and visible wavelength range. To produce consistent global datasets the algorithm needs to have good global performance, while short computation time facilitates the use of the algorithm in near real time applications. To test the global performance of the algorithm we look at the convergence behaviour as diagnostic tool of the ozone profile retrievals from the GOME instrument (on board ERS-2) for February and October 1998. In this way, we uncover different classes of retrieval problems, related to the South Atlantic Anomaly, low cloud fractions over deserts, desert dust outflow over the ocean, and the intertropical convergence zone. The influence of the first guess and the external input data including the ozone cross-sections and the ozone climatologies on the retrieval performance is also investigated. By using a priori ozone profiles which are selected on the expected total ozone column, retrieval problems due to anomalous ozone distributions (such as in the ozone hole) can be avoided. By applying the algorithm adaptations the convergence statistics improve considerably, not only increasing the number of successful retrievals, but also reducing the average computation time, due to less iteration steps per retrieval. For February 1998, non-convergence was brought down from 10.7% to 2.1%, while the mean number of iteration steps (which dominates the computational time) dropped 26% from 5.11 to 3.79.

Tropospheric O3 column estimates are produced and evaluated from spaceborne O3 observations by the subtraction of assimilated O3 profile observations from total column observations, the so-called Tropospheric O3 ReAnalysis or TORA method. Here we apply the TORA method to six years (1996–2001) of ERS-2 GOME/TOMS total O3 and ERS-2 GOME O3 profile observations using the TM5 global chemistry-transport model with a linearized O3 photochemistry parameterization scheme. Free running TM5 simulations show good agreement with O3 sonde observations in the upper-tropospheric and lower stratospheric region (UTLS), both for short day-to-day variability as well as for monthly means. The assimilation of GOME O3 profile observations counteracts the mid-latitude stratospheric O3 drift caused by the overstrong stratospheric meridional circulation in TM5. Assimilation of GOME O3 profile observations also improves the bias and correlations in the tropical UTLS region but slightly degrades the model-to-sonde correlations and bias of extra-tropical UTLS. We suggest that this degradation is related to the large ground pixel size of the GOME O3 measurements (960×100 km) in combination with retrieval and calibration errors. The added value of the assimilation of GOME O3 profiles compared to stand-alone model simulations lays in the long term variations of stratospheric O3, not in short term synoptic variations. The evaluation of daily and monthly tropospheric O3 columns obtained from total column observations and using the TORA methodology shows that the use of GOME UV-VIS nadir O3 profiles in combination with the spatial resolution of the model does not result in satisfactory residual tropospheric ozone columns.

We present the configuration of the Météo-France Chemistry and Transport Model (CTM) MOCAGE-Climat that will be dedicated to the study of chemistry and climate interactions. MOCAGE-Climat is a state-of-the-art CTM that simulates the global distribution of ozone and its precursors (82 chemical species) both in the troposphere and the stratosphere, up to the mid-mesosphere (~70 km). Surface processes (emissions, dry deposition), convection, and scavenging are explicitly described in the model that has been driven by the ECMWF operational analyses of the period 2000–2005, on T21 and T42 horizontal grids and 60 hybrid vertical levels, with and without a procedure that reduces calculations in the boundary layer, and with on-line or climatological deposition velocities. Model outputs have been compared to available observations, both from satellites (TOMS, HALOE, SMR, SCIAMACHY, MOPITT) and in-situ instrument measurements (ozone sondes, MOZAIC and aircraft campaigns) at climatological timescales. The distribution of long-lived species is in fair agreement with observations in the stratosphere putting aside the shortcomings associated with the large-scale circulation. The variability of the ozone column, both spatially and temporarily, is satisfactory. However, because the Brewer-Dobson circulation is too fast, too much ozone is accumulated in the lower to mid-stratosphere at the end of winter. Ozone in the UTLS region does not show any systematic bias. In the troposphere better agreement with ozone sonde measurements is obtained at mid and high latitudes than in the tropics and differences with observations are the lowest in summer. Simulations using a simplified boundary layer lead to larger ozone differences between the model and the observations up to the mid-troposphere. NOx in the lowest troposphere is in general overestimated, especially in the winter months over the Northern Hemisphere, which may result from a positive bias in OH. Dry deposition fluxes of O3 and nitrogen species are within the range of values reported by recent inter-comparison model exercises. The use of climatological deposition velocities versus deposition velocities calculated on-line had greatest impact on HNO3 and NO2 in the troposphere.

We present a new algorithm for the near-real time retrieval – within 3 h of the actual satellite measurement – of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI). The retrieval is based on the combined retrieval-assimilation-modelling approach developed at KNMI for off-line tropospheric NO2 from the GOME and SCIAMACHY satellite instruments. We have adapted the off-line system such that the required a priori information – profile shapes and stratospheric background NO2 – is now immediately available upon arrival (within 80 min of observation) of the OMI NO2 slant columns and cloud data at KNMI. Slant columns for NO2 are retrieved using differential optical absorption spectroscopy (DOAS) in the 405–465 nm range. Cloud fraction and cloud pressure are provided by a new cloud retrieval algorithm that uses the absorption of the O2-O2 collision complex near 477 nm. On-line availability of stratospheric slant columns and NO2 profiles is achieved by running the TM4 chemistry transport model (CTM) forward in time based on forecast ECMWF meteo and assimilated NO2 information from all previously observed orbits. OMI NO2 slant columns, after correction for spurious across-track variability, show a random error for individual pixels of approximately 0.7×1015 molec cm−2. Cloud parameters from OMI's O2-O2 algorithm have similar frequency distributions as retrieved from SCIAMACHY's Fast Retrieval Scheme for Cloud Observables (FRESCO) for August 2006. On average, OMI cloud fractions are higher by 0.011, and OMI cloud pressures exceed FRESCO cloud pressures by 60 hPa. A sequence of OMI observations over Europe in October 2005 shows OMI's capability to track changeable NOx air pollution from day to day in cloud-free situations.