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This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

International audience; BACKGROUND AND AIM: Previous studies have reported a decrease in air pollution following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference, and did not assess the role of different policy interventions. In this contribution, we quantitatively evaluated the association between various lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities and the associated short-term mortality in the period of February-July 2020. METHODS: We used data from several chemical transport models developed by the Copernicus Atmosphere Monitoring Service (CAMS) to define trends in air pollution under business-as-usual and lockdown scenarios, thus removing differences due to weather conditions and other differences affecting pre-post comparisons. We then applied an advanced spatio-temporal Bayesian non-linear mixed effect model to determine the association with stringency indices of individual policy measures, allowing non-linear relationships and geographical correlations. RESULTS: The findings indicate evidence of non-linear relationships, with a stronger decrease in NO2 and to a lesser extent PMs under very strict lockdown regimes. The effects of lockdown measures vary geographically, with a stronger decline in pollution in Southern and Central Europe. The comparative analysis of separate lockdown policies suggests important differences across interventions. Specifically, actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements had strong effects, while restrictions on internal movement and international travels showed little impact. The observed decrease in pollution potentially resulted in hundreds of avoided deaths across the European cities. CONCLUSIONS: This study provides important evidence on the differential impacts of various policies implemented during the COVID-19 pandemic in decreasing the level of pollutants in urban areas across Europe.

The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis dataset of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of three-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding 1 year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO; tropospheric column NO2; aerosol optical depth (AOD); and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF's Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared with most of the independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations used for validation in this paper than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.

Global multiconstituent concentration and emission fields obtained from the assimilation of the satellite retrievals of ozone, CO, NO2, HNO3, and SO2 from the Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment 2, Measurements of Pollution in the Troposphere, Microwave Limb Sounder, and Atmospheric Infrared Sounder (AIRS)/OMI are used to understand the processes controlling air pollution during the Korea‐United States Air Quality (KORUS‐AQ) campaign. Estimated emissions in South Korea were 0.42 Tg N for NOx and 1.1 Tg CO for CO, which were 40% and 83% higher, respectively, than the a priori bottom‐up inventories, and increased mean ozone concentration by up to 7.5 ± 1.6 ppbv. The observed boundary layer ozone exceeded 90 ppbv over Seoul under stagnant phases, whereas it was approximately 60 ppbv during dynamical conditions given equivalent emissions. Chemical reanalysis showed that mean ozone concentration was persistently higher over Seoul (75.10 ± 7.6 ppbv) than the broader KORUS‐AQ domain (70.5 ± 9.2 ppbv) at 700 hPa. Large bias reductions (>75%) in the free tropospheric OH show that multiple‐species assimilation is critical for balanced tropospheric chemistry analysis and emissions. The assimilation performance was dependent on the particular phase. While the evaluation of data assimilation fields shows an improved agreement with aircraft measurements in ozone (to less than 5 ppbv biases), CO, NO2, SO2, PAN, and OH profiles, lower tropospheric ozone analysis error was largest at stagnant conditions, whereas the model errors were mostly removed by data assimilation under dynamic weather conditions. Assimilation of new AIRS/OMI ozone profiles allowed for additional error reductions, especially under dynamic weather conditions. Our results show the important balance of dynamics and emissions both on pollution and the chemical assimilation system performance., Key Points Multiconstituent data assimilation during KORUS‐AQ showed that emissions in South Korea were 0.42 Tg N for NOx and 1.1 Tg CO for COThese emissions were 40% and 83% higher, respectively, than the a priori bottom‐up inventories and increased ozone by up to 7.5 ± 1.6 ppbvMean ozone concentration was persistently higher over Seoul (75.1 ± 7.6 ppbv) than the broader KORUS‐AQ domain (70.5 ± 9.2 ppbv) at 700 hPa

The boreal summers of 2019 and 2020 were witness to extensive high northern latitude wildfire activity, most notably within the Arctic Circle across eastern Russia. Near-real-time monitoring of the wildfire activity, based on satellite observations of active fires, showed widespread and persistent fires at a scale that had not been observed in the previous years that satellite observations are available. The European Centre for Medium-Range Weather Forecasts (ECMWF) through its operation of, and contribution to, different Copernicus Services is in a unique position to provide detailed information to monitor high-latitude wildfire activity, including their evolution and potential impacts, when they occur. Fire weather forecasts from the Copernicus Emergency Management Service (CEMS), and surface climate anomalies from the Copernicus Climate Change Service (C3S) both provide context to the environmental conditions required for wildfires to persist. Analyses based on observations of fire radiative power, along with analyses and forecasts of associated atmospheric pollutants, from the Copernicus Atmosphere Monitoring Service (CAMS) aid in quantifying the scale and intensity in near-real-time and the subsequent atmospheric impacts. We present an analysis of Arctic and high northern latitude wildfires during the summers of 2019 and 2020, reviewing the underlying meteorological/climatological conditions, the estimated emissions and transport of smoke constituents over the Arctic Ocean. We will show that the different datasets, while being relatively independent, show a strong correspondence and provide a wealth of information required to monitor and provide context for wildfire activity.

In this study we present a novel monitoring methodology to detect local CH4 concentration anomalies worldwide that are related to rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget. The method uses high resolution (7 km × 7 km) retrievals of total column CH4 from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 Precursor satellite. Observations are combined with high resolution CH4 forecasts (~ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) close to the native satellite resolution at appropriate time. Investigating the departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the large-scale biases on both forecasts and satellite observations. We then use a simple classification on the filtered departures to detect anomalies and plumes coming from CAMS emissions that are missing (e.g. pipeline or facility leaks), under-reported or over-reported (e.g. depleted drilling fields). Additionally, the classification helps to detect local satellite retrieval errors due to land surface albedo issues.

Measurements of atmospheric column-averaged dry-air mole fractions of carbon dioxide ( XCO2 ), methane ( XCH4 ), and carbon monoxide ( XCO ) have been collected across the Pacific Ocean during the Measuring Ocean REferences 2 (MORE-2) campaign in June 2019. We deployed a shipborne variant of the EM27/SUN Fourier transform spectrometer (FTS) on board the German R/V Sonne which, during MORE-2, crossed the Pacific Ocean from Vancouver, Canada, to Singapore. Equipped with a specially manufactured fast solar tracker, the FTS operated in direct-sun viewing geometry during the ship cruise reliably delivering solar absorption spectra in the shortwave infrared spectral range (4000 to 11000 cm−1 ). After filtering and bias correcting the dataset, we report on XCO2 , XCH4 , and XCO measurements for 22 d along a trajectory that largely aligns with 30 ∘ N of latitude between 140 ∘ W and 120 ∘ E of longitude. The dataset has been scaled to the Total Carbon Column Observing Network (TCCON) station in Karlsruhe, Germany, before and after the MORE-2 campaign through side-by-side measurements. The 1σ repeatability of hourly means of XCO2 , XCH4 , and XCO is found to be 0.24 ppm , 1.1 ppb , and 0.75 ppb , respectively. The Copernicus Atmosphere Monitoring Service (CAMS) models gridded concentration fields of the atmospheric composition using assimilated satellite observations, which show excellent agreement of 0.52±0.31 ppm for XCO2 , 0.9±4.1 ppb for XCH4 , and 3.2±3.4 ppb for XCO (mean difference ± SD, standard deviation, of differences for entire record) with our observations. Likewise, we find excellent agreement to within 2.2±6.6 ppb with the XCO observations of the TROPOspheric MOnitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite (S5P). The shipborne measurements are accessible at https://doi.org/10.1594/PANGAEA.917240 ( Knapp et al. , 2020 ) .

The Korea-United States Air Quality Study (KORUS-AQ) took place in spring 2016 to better understand air pollution in Korea. In support of KORUS-AQ, 2554 whole air samples (WAS) were collected aboard the NASA DC-8 research aircraft and analyzed for 82 C1–C10 volatile organic compounds (VOCs) using multi-column gas chromatography. Together with fast-response measurements from other groups, the air samples were used to characterize the VOC composition in Seoul and surrounding regions, determine which VOCs are major ozone precursors in Seoul, and identify the sources of these reactive VOCs. (1) The WAS VOCs showed distinct signatures depending on their source origins. Air collected over Seoul had abundant ethane, propane, toluene and n-butane while plumes from the Daesan petrochemical complex were rich in ethene, C2–C6 alkanes and benzene. Carbonyl sulfide (COS), CFC-113, CFC-114, carbon tetrachloride (CCl4) and 1,2-dichloroethane were good tracers of air originating from China. CFC-11 was also elevated in air from China but was surprisingly more elevated in air over Seoul. (2) Methanol, isoprene, toluene, xylenes and ethene were strong individual contributors to OH reactivity in Seoul. However methanol contributed less to ozone formation based on photochemical box modeling, which better accounts for radical chemistry. (3) Positive Matrix Factorization (PMF) and other techniques indicated a mix of VOC source influences in Seoul, including solvents, traffic, biogenic, and long-range transport. The solvent and traffic sources were roughly equal using PMF, and the solvents source was stronger in the KORUS-AQ emission inventory. Based on PMF, ethene and propene were primarily associated with traffic, and toluene, ethylbenzene and xylenes with solvents, especially non-paint solvents for toluene and paint solvents for ethylbenzene and xylenes. This suggests that VOC control strategies in Seoul could continue to target vehicle exhaust and paint solvents, with additional regulations to limit the VOC content in a variety of non-paint solvents.

The CO2 Human Emission (CHE) project is an European initiative bringing together a consortium of 22 European partners to build a prototype global CO2 source inversion system that can provide policy-relevant information on the spatiotemporal characteristics of anthropogenic CO2 emissions. This prototype shall evolve toward a new Copernicus CO2 service, which will provide a Monitoring and Verification Support (MVS) capacity that can address the challenge of the global stocktake (GST) devised under the Paris Agreement. The global inversion system will build on existing operational infrastructures (CAMS, C3S) at the European Centre for Medium-range Weather Forecast (ECMWF) to exploit ground-based measurements as well as space-based observations from current and future satellite missions (e.g., Sentinel 5p and CO2M). We will present ongoing efforts at ECMWF to develop a source inversion capability in the current operational Integrated Forecasting System (IFS), which will serve as the basis for the future global CO2 inversion prototype. Preliminary results will be discussed, that include model transport error estimations based on Monte-Carlo ensemble simulations as well as the first chemical source optimization experiments performed with the IFS 4D-Var system.

As part of the Copernicus Atmosphere Monitoring Service (CAMS), operated by ECMWF on behalf of the European Commission, global analyses and forecasts of atmospheric composition have been produced operationally since 2015. These were built on many years of previous work under the GEMS and MACC projects, which began producing regular forecasts in 2007.Since the transition to an operational service, there have continued to be many new developments and improvements to the system in five major upgrades, including increased horizontal and vertical resolution, updated emissions and paramterisations, additional species such as nitrate aerosol, as well as updates to the underlying meteorological model and data assimilation. The components of this system (aerosols, gas-phase chemistry, meteorology and the ocean) are also now coupled more tightly via active feedbacks then ever before.In this interactive presentation, we will demonstrate the impact of a number of these developments on the performance of the resulting global air quality forecasts, alongside the continuing evolution of our approaches to assessing model improvement against independent in-situ and remote-sensing observations from a variety of platforms.Because the continuing evolution of an operational system can make the analysis of long-term trends problematic, we will also contrast this with the CAMS global reanalysis product, which (while not using the very latest version of the model) do provide a consistent long-term dataset from 2003 onwards.

Effective monitoring of global wildfire activity requires comprehensive knowledge of changing environmental (including atmospheric and hydrological) conditions, fuel availability and routine observations of fire locations and intensity. The European Centre for Medium-Range Weather Forecasts (ECMWF) through its operation of, and contribution to, different Copernicus Services is in a unique position to provide detailed information on the conditions leading to wildland fire activity, the evolution of wildfires, and their potential impacts, when they occur. Fire weather forecasts from the Copernicus Emergency Management Service, and surface climate anomalies from the Copernicus Climate Change Service both provide context to the environmental conditions required for wildfires to persist. Analyses based on observations of fire radiative power, along with analyses and forecasts of associated atmospheric pollutants, from the Copernicus Atmosphere Monitoring Service aid in quantifying the scale and intensity in near-real-time and the subsequent atmospheric impacts. During 2019, regions of anomalously hot and dry surface conditions in Arctic Siberia and southeast Australia experienced large-scale, long-duration wildfires which burned thousands of square kilometres with a total intensity that was significantly above the average of the previous 16 years of data in those regions. We present an overview of the evolution of fire activity in Siberia between June-August 2019, and Australia between September 2019-January 2020, based on ECMWF/Copernicus data for fire weather, climate anomalies and active fires. We will show that the different datasets, while being relatively independent, show a strong correspondence and provide a wealth of information vital to understanding global wildfires, their underlying causes and environmental impacts.

This presentation reports the findings of a multi-model pre-mission study in preparation for an airborne field campaign to investigate the upper troposphere and lower stratosphere (UTLS) composition under the influence of the Asian summer monsoon (ASM). The NSF/NASA supported airborne study is planned for the western Pacific atmosphere during July-August 2020 using a base in Okinawa, Japan. The pre-mission study uses three chemistry-transport models (i.e., NASA GSFC GEOS5, NCAR WACCM, and ECMWF CAMS) to investigate transport patterns and gas and aerosol chemical composition in the campaign region UTLS during the 2019 ASM period. In addition, artificial surface tracers from the WRF model helped identify the locations and evolution of rapid convective uplifting from regional sources. The impact of one typhoon occurrence during this 2019 ASM period will be discussed. Together, the multi-model results support the hypotheses of the ACCLIP campaign which identifies the western Pacific as a significant pathway for reactive chemical pollutants and climate relevant emissions from the ASM to enter the global UTLS.

Validation opportunities for model data and satellite observations in the short-wave infra-red spectral range are still sparse above the oceans. To provide such opportunities, we qualify a Fourier-transform spectrometer (FTS) for the regular use on ships. We use the EM27/SUN FTS [1] in direct-sunlight measurement geometry to retrieve total column densities of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) [2] with solar absorption spectroscopy.Performing direct-sunlight measurements from a moving platform poses significant challenges to the solar tracking. We use a solar tracker that compensates the vessel's movements in real time, keeping the pointing of the instrument relative to the center of the sun better than 0.05° for more than 99 % of the time [3]. The solar tracker is part of a newly developed enclosure that allows automated measurements and withstands environmental factors such as rain, humidity, and sea spray.The instrument was deployed on board the German research vessel RV Sonne during the MORE-2 (Measuring Oceanic REferences 2) campaign on a longitudinal transect from Vancouver (Canada) to Singapore in June 2019. During the campaign we recorded 33800 direct sunlight spectra from which column-averaged dry-air mole fractions of CO2, CH4, and CO are retrieved. Our results are calibrated against World Meteorological Organization standards and the columns achieve a relative precision of 0.06 %, 0.06 %, and 1.02 % for CO2, CH4, and CO, respectively.We compare our records to coincident observations of the Greenhouse gases Observing SATellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and the TROPOspheric Monitoring Instrument (TROPOMI). Our CO2 records show a mean offset of -3.2 ± 1.1 ppm to OCO-2 and -1.4 ± 1.7 ppm to GOSAT observations. Furthermore, we find a mean CH4 offset of 17 ± 6 ppb to GOSAT and a mean CO offset of 3.5 ± 2.6 ppb to TROPOMI. The Copernicus Atmosphere Monitoring Service (CAMS) provided us with model data of CH4 and CO. We could show that the CO data agree well with our measurements, showing an offset of 3.5 ± 3.6 ppb.[1] Gisi, M. et al.: XCO2-measurements with a tabletop FTS using solar absorption spectroscopy, Atmos. Meas. Tech., 5, 2969-2980, 2012[2] Hase, F. et al.: Addition of a channel for XCO observations to a portable FTIR spectrometer for greenhouse gas measurements, Atmos. Meas. Tech., 9, 2303-2313, 2016[3] Klappenbach, F. et al.: Accurate mobile remote sensing of XCO2 and XCH4 latitudinal transects from aboard a research vessel, Atmos. Meas. Tech., 8, 5023–5038, 2015

The Copernicus Atmosphere Monitoring Service (CAMS, atmosphere.copernicus.eu) led by ECMWF is one of the major users of TROPOMI data. TROPOMI ozone retrievals have been routinely assimilated in the operational CAMS system since December 2018 and help CAMS to provide good quality daily ozone analyses and 5-day forecasts. CO, NO2, HCHO and SO2 retrievals from TROPMI are currently monitored in the operational CAMS system and CH4 in the CAMS GHG system. This means that the data are routinely compared with the CAMS atmospheric composition fields, but do not influence the CAMS analyses yet. Howerver, assimilation tests with TROPOMI CO, NO2, SO2 and CH4 data are ongoing and it is hoped that the routine assimilation of these species in the CAMS system can begin later this year. In this presentation we will give an update on the use or TROPOMI data in the CAMS system and show the latest results from the monitoring and asimilation tests carried out with the TROPOMI data by CAMS.

The Copernicus Atmosphere Monitoring Service (CAMS) produces operationally global 5-day forecast of atmospheric composition and the weather using ECMWF’s Integrated Forecasting System (IFS) since 2015.Beginning with a system upgrade in June 2018 (45r1), the ozone and aerosol fields have been used in the radiation scheme to account for their radiative impact in the global CAMS forecasts. This approach replaced an aerosol and ozone climatology, which had been used before and which is still used in ECMWF's operational high-resolution medium-range NWP forecasts. The CAMS forecast system, which runs at a resolution of about 40 km, is applied here as a test-bed to explore the importance of aerosol direct feedback in an operational configuration, which can guide developments on composition-weather feedbacks for ECMWF's medium-range, monthly and seasonal forecasts.We will discuss the changes and improvements of temperature forecast errors (i) using typical NWP scores and (ii) by applying an event based approach that focuses on episodes of high aerosol burdens such as the transport of Sahara dust to Europe during the heatwave in June 2019. In more detail we will show to what extent the realism of the prognostic aerosol fields influences the temperature response by considering aerosol forecast which were, or were not, improved by data assimilation of aerosol optical depth at the start of the forecast. We will further demonstrate that the consistent updates of both the climatological and prognostic aerosol fields are an important prerequisite for a making progress.

The Copernicus Atmosphere Monitoring Service (CAMS) delivers a wealth of information on atmospheric composition change over short to long timescales. One of the core products of CAMS regards short term air quality forecasts with a three days lead time as well as reanalyses over the past years for the European region.This service is covered by the CAMS_50 project which is now operational since 2015. It relies on a distributed production of 9 individual air quality models, consolidated by a centralised regional production unit at Météo-France before delivery to the European Centre on Medium Range Meteorological Forecasts, which implements the CAMS service.Each model is operated by its own development team across Europe, all of them deliver air quality forecasts covering the whole continent at 10km resolution. The modelling team currently operational are at present: CHIMERE (France), DEHM (Denmark), EMEP/MSC-W (Norway), EURAD-IM (Germany), GEM-AQ (Poland), LOTOS-EUROS (The Netherlands), MATCH (Sweden), MOCAGE (France), SILAM (Finland). Two additional models are now applying to join the ensemble: MINNI (Italy), and MONARCH (Spain).Such an ensemble of different models offers excellent complementarity in model capabilities as demonstrated by the performances of the ENSEMBLE product. It also leads to substantial challenges in coordinated model development. We will present the main recent achievements, status, and future plans for the validation and development of models underlying the service.

The European Union’s Copernicus Atmosphere Monitoring Service (CAMS) operationally provides daily forecasts of global atmospheric composition. It uses the ECMWF Integrated Forecasting System (IFS), which includes meteorological and atmospheric composition variables, such as reactive gases, greenhouse gases and aerosols, for its global forecasts and reanalyses. The current green-house gases operational suite monitors CH4 and CO2 and assimilates TANSO and IASI retrievals for both species. The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) satellite launched in October 2017 yields a wealth of atmospheric composition data, including CH4 retrievals at unprecedented high horizontal resolution (7km) and up to daily revisit time. We used the IFS to perform monitoring experiments at different horizontal resolutions (25 km and 9 km). We also performed first data assimilation experiments at 25 km horizontal resolution.This first set of monitoring experiments shows the potential of the TROPOMI CH4 retrievals to correct known biases that exist in the current CAMS analyses and forecasts. Assimilation experiments of TROPOMI CH4 shows that adding the instrument in the operational chain would significantly improve the analysis and forecasts. Detection of CH4 sources seen by TROPOMI compared to CAMS also shows the potential of the instrument to inform on and infer anthropogenic and natural sources. For example, discrepancies between TROPOMI retrievals and CAMS fields in the CH4 levels associated with oil and gas extraction activities show very promising perspectives for monitoring and analysis of CH4 concentration and emissions. We will finally discuss the challenges and progress made towards performing inversions using the IFS operational system.

The Copernicus Atmosphere Monitoring Service (CAMS) is one of the six thematic services of the European Union's flagship Earth Observation programme Copernicus. CAMS is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) together with a network of about 60 entities distributed in 20 European countries: it is thus a truly international and collaborative effort. CAMS delivers operationally a wide range of information products and services on air quality, emissions and surface fluxes, solar radiation and climate forcings. All CAMS atmospheric composition outputs are unrestricted and provided free-of-charge to users worldwide. The CAMS global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used successfully for Numerical Weather Prediction and has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. Over 60 different satellite data streams are assimilated to provide twice daily global atmosphere composition forecasts.

By the end of the current decade, there are plans to deploy several geostationary Earth orbit (GEO) satellite missions for atmospheric composition over North America, East Asia and Europe with additional missions proposed. Together, these present the possibility of a constellation of geostationary platforms to achieve continuous time-resolved high-density observations over continental domains for mapping pollutant sources and variability at diurnal and local scales. In this paper, we use a novel approach to sample a very high global resolution model (GEOS-5 at 7 km horizontal resolution) to produce a dataset of synthetic carbon monoxide pollution observations representative of those potentially obtainable from a GEO satellite constellation with predicted measurement sensitivities based on current remote sensing capabilities. Part 1 of this study focuses on the production of simulated synthetic measurements for air quality OSSEs (Observing System Simulation Experiments). We simulate carbon monoxide nadir retrievals using a technique that provides realistic measurements with very low computational cost. We discuss the sampling methodology: the projection of footprints and areas of regard for geostationary geometries over each of the North America, East Asia and Europe regions; the regression method to simulate measurement sensitivity; and the measurement error simulation. A detailed analysis of the simulated observation sensitivity is performed, and limitations of the method are discussed. We also describe impacts from clouds, showing that the efficiency of an instrument making atmospheric composition measurements on a geostationary platform is dependent on the dominant weather regime over a given region and the pixel size resolution. These results demonstrate the viability of the "instrument simulator" step for an OSSE to assess the performance of a constellation of geostationary satellites for air quality measurements.

Understanding changes in the burden and growth rate of atmospheric methane (CH4) has been the focus of several recent studies but still lacks scientific consensus. Here we investigate the role of decreasing anthropogenic carbon monoxide (CO) emissions since 2002 on hydroxyl radical (OH) sinks and tropospheric CH4 loss. We quantify this impact by contrasting two model simulations for 2002–2013: (1) a Measurement of the Pollution in the Troposphere (MOPITT) CO reanalysis and (2) a Control-Run without CO assimilation. These simulations are performed with the Community Atmosphere Model with Chemistry of the Community Earth System Model fully coupled chemistry climate model with prescribed CH4 surface concentrations. The assimilation of MOPITT observations constrains the global CO burden, which significantly decreased over this period by ~20%. We find that this decrease results to (a) increase in CO chemical production, (b) higher CH4 oxidation by OH, and (c) ~8% shorter CH4 lifetime. We elucidate this coupling by a surrogate mechanism for CO-OH-CH4 that is quantified from the full chemistry simulations.

Due to its impact on environment, tropospheric ozone received particular attention since several decades. Ground-based networks associated with regional chemical transport models are used to monitor and forecast surface ozone concentrations, but coverage, representativeness, and accuracy issues remain important. Recent satellite observations have demonstrated the capacity to probe tropospheric ozone, but there has been no explicit attempt to quantify their ability to measure ozone pollution near ground. We propose here to assess the ability of ozone sounders to detect a photochemical ozone pollution event that is supposed to be a favorable situation for satellite detection. We have chosen ozone pollution event over Europe associated with a warm conveyor belt that efficiently transports photochemically produced ozone upward. Ozone satellite products from Global Ozone Monitoring Experiment-2, Infrared Atmospheric Sounding Interferometer (IASI), and Ozone Monitoring Instrument are analyzed here for their capacity to capture such an event. Also, in situ observations and regional chemical-transport models show increasing ozone concentrations in the continental and Mediterranean boundary layer and further transport to central Europe and Scandinavia associated with upward transport. Satellite observations do not detect high ozone concentrations within the boundary layer due the weak sensitivity near the surface. Nevertheless, we have shown that the IR sounder IASI was able to detect, qualitatively and quantitatively, the ozone plume transported upward by the warm conveyor belt, suggesting that a quantification of upward transport of ozone pollution could be possible using current satellite observations. This should encourage us to further explore approaches more sensitive to surface ozone (such as the multispectral approach) and to prepare the next generation of still more sensitive spaceborne instruments.

The behavior of the extratropical transition layer (ExTL) is investigated using a chemistry transport model (CTM) and analyses derived from assimilation of MLS (Microwave Limb Sounder) O3 and MOPITT (Measurements Of Pollution In The Troposphere) CO data. We firstly focus on a stratosphere–troposphere exchange (STE) case study that occurred on 15 August 2007 over the British Isles (50° N, 10° W). We evaluate the effect of data assimilation on the O3–CO correlations. It is shown that data assimilation disrupts the relationship in the transition region. When MLS O3 is assimilated, CO and O3 values are not consistent between each other, leading to unphysical correlations at the STE location. When MLS O3 and MOPITT CO assimilated fields are taken into account in the diagnostics the relationship happens to be more physical. We then use O3–CO correlations to quantify the effect of data assimilation on the height and depth of the ExTL. When the free-model run O3 and CO fields are used in the diagnostics, the ExTL distribution is found 1.1 km above the thermal tropopause and is 2.6 km wide (2σ). MOPITT CO analyses only slightly sharpen (by −0.02 km) and lower (by −0.2 km) the ExTL distribution. MLS O3 analyses provide an expansion (by +0.9 km) of the ExTL distribution, suggesting a more intense O3 mixing. However, the MLS O3 analyses ExTL distribution shows a maximum close to the thermal tropopause and a mean location closer to the thermal tropopause (+0.45 km). When MLS O3 and MOPITT CO analyses are used together, the ExTL shows a mean location that is the closest to the thermal tropopause (+0.16 km). We also extend the study at the global scale on 15 August 2007 and for the month of August 2007. MOPITT CO analyses still show a narrower chemical transition between stratosphere and troposphere than the free-model run. MLS O3 analyses move the ExTL toward the troposphere and broaden it. When MLS O3 analyses and MOPITT CO analyses are used together, the ExTL matches the thermal tropopause poleward of 50°.

Stratospheric ozone profiles from MLS and tropospheric ozone columns from IASI have been assimilated into the MOCAGE model during the month of July 2009, using a variational (3D-FGAT) technique. This study compares the separate and combined analysis of IASI tropospheric columns and MLS stratospheric profiles, in order to investigate possible synergistic effects. The contributions on the ozone distribution of each data assimilation experiment are discussed and self-consistency is evaluated via χ 2 test, Observations minus Analyses and Observations minus Forecasts diagnostics. The results show that MLS assimilation has a significant impact on the model troposphere. An evaluation of the stratospheric distribution is made using independent MIPAS stratospheric profiles. IAGOS flights are used to evaluate the impact of the dataset assimilated in the troposphere and in the UTLS region. Comparisons to MIPAS independent observations show an improvement of the ozone vertical profile in the stratosphere and in the UTLS due to the assimilation of MLS observations. The IASI analyses show the strongest improvements on ozone distributions in the free troposphere. The combined assimilation shows the most realistic ozone fields overall in the stratosphere, UTLS and troposphere. Though neither instrument covers the entire atmospheric column alone, the combined MLS and IASI analyses show also the best agreement with the independent total ozone columns data from the OMI dataset. Bias, RMSE and correlation are significantly improved compared to the free running model. This set of validations show that the vertical structure of the ozone fields is strongly improved by assimilation. By combining the pieces of information brought by IASI and MLS in the analyses, combined assimilation provides highly realistic ozone fields.

© 2016 Elsevier Ltd This paper describes the second phase of an Observing System Simulation Experiment (OSSE) that utilizes the synthetic measurements from a constellation of satellites measuring atmospheric composition from geostationary (GEO) Earth orbit presented in part I of the study. Our OSSE is focused on carbon monoxide observations over North America, East Asia and Europe where most of the anthropogenic sources are located. Here we assess the impact of a potential GEO constellation on constraining northern hemisphere (NH) carbon monoxide (CO) using data assimilation. We show how cloud cover affects the GEO constellation data density with the largest cloud cover (i.e., lowest data density) occurring during Asian summer. We compare the modeled state of the atmosphere (Control Run), before CO data assimilation, with the known “true” state of the atmosphere (Nature Run) and show that our setup provides realistic atmospheric CO fields and emission budgets. Overall, the Control Run underestimates CO concentrations in the northern hemisphere, especially in areas close to CO sources. Assimilation experiments show that constraining CO close to the main anthropogenic sources significantly reduces errors in NH CO compared to the Control Run. We assess the changes in error reduction when only single satellite instruments are available as compared to the full constellation. We find large differences in how measurements for each continental scale observation system affect the hemispherical improvement in long-range transport patterns, especially due to seasonal cloud cover. A GEO constellation will provide the most efficient constraint on NH CO during winter when CO lifetime is longer and increments from data assimilation associated with source regions are advected further around the globe.

© 2016. American Geophysical Union. All Rights Reserved. We examine in detail a 1 year global reanalysis of carbon monoxide (CO) that is based on joint assimilation of conventional meteorological observations and Measurement of Pollution in The Troposphere (MOPITT) multispectral CO retrievals in the Community Earth System Model (CESM). Our focus is to assess the impact to the chemical system when CO distribution is constrained in a coupled full chemistry-climate model like CESM. To do this, we first evaluate the joint reanalysis (MOPITT Reanalysis) against four sets of independent observations and compare its performance against a reanalysis with no MOPITT assimilation (Control Run). We then investigate the CO burden and chemical response with the aid of tagged sectoral CO tracers.We estimate the total tropospheric CO burden in 2002 (from ensemble mean and spread) to be 371 ± 12%Tg for MOPITT Reanalysis and 291 ± 9%Tg for Control Run. Our multispecies analysis of this difference suggests that (a) direct emissions of CO and hydrocarbons are too low in the inventory used in this study and (b) chemical oxidation, transport, and deposition processes are not accurately and consistently represented in the model. Increases in CO led to net reduction of OH and subsequent longer lifetime of CH4 (Control Run: 8.7 years versus MOPITT Reanalysis: 9.3 years). Yet at the same time, this increase led to 5-10% enhancement of Northern Hemisphere O3 and overall photochemical activity via HOx recycling. Such nonlinear effects further complicate the attribution to uncertainties in direct emissions alone. This has implications to chemistry-climate modeling and inversion studies of longer-lived species.

We assimilate stratospheric ozone profiles from MLS (Microwave Limb Sounder) into the MOCAGE Chemistry Transport Model (CTM) to study Stratosphere-Troposphere Exchange (STE). This study uses two horizontal grid resolutions of 2° and 0.2°. The combined impacts of MLS ozone assimilation and high horizontal resolution are illustrated in two case studies where STE events occurred (23 June 2009 and 17 July 2009). At high resolution the filamentary structures of stratospheric air which characterise STE events are captured by the model. To test the impact of the assimilation and the resolution, we compare model outputs from different experiments (high resolution and low resolution; MLS assimilation run and free run) with independent data (MOZAIC aircraft ozone data; WOUDC ozone sonde network data). MLS ozone analyses show a better description of the Upper Troposphere Lower Stratosphere (UTLS) region and the stratospheric intrusions than the free model run. In particular, at high horizontal resolution the MLS ozone analyses present realistic filamentary ozone structures in the UTLS and laminae structures in the ozone profile. Despite a low aspect ratio between horizontal resolution and vertical resolution in the UTLS at high horizontal resolution, MLS ozone analyses improve the vertical structures of the ozone fields. Results from backward trajectories and ozone forecasts show that assimilation at high horizontal resolution of MLS ozone profiles between 10 hPa and 215 hPa has an impact on tropospheric ozone.

This paper presents observing system simulation experiments (OSSEs) to compare the relative capabilities of two geostationary thermal infrared (TIR) instruments to measure ozone (O3) and carbon monoxide (CO) for monitoring air quality (AQ) over Europe. The primary motivation of this study is to use OSSEs to assess how these infrared instruments can constrain different errors affecting AQ hindcasts and forecasts (emissions, meteorology, initial condition and the 3 parameters together). The first instrument (GEO-TIR) has a configuration optimized to monitor O3 and CO in the lowermost troposphere (LmT; defined to be the atmosphere between the surface and 3 km), and the second instrument (GEO-TIR2) is designed to monitor temperature and humidity. Both instruments measure radiances in the same spectral TIR band. Results show that GEO-TIR could have a significant impact (GEO-TIR is closer to the reference atmosphere than GEO-TIR2) on the analyses of O3 and CO LmT column. The information added by the measurements for both instruments is mainly over the Mediterranean Basin and some impact can be found over the Atlantic Ocean and Northern Europe. The impact of GEO-TIR is mainly above 1 km for O3 and CO but can also improve the surface analyses for CO. The analyses of GEO-TIR2 show low impact for O3 LmT column but a significant impact (although still lower than for GEO-TIR) for CO above 1 km. The results of this study indicate the beneficial impact from an infrared instrument (GEO-TIR) with a capability for monitoring O3 and CO concentrations in the LmT, and quantify the value of this information for constraining AQ models.

© 2015. American Geophysical Union. All Rights Reserved. We show the results and evaluation with independent measurements from assimilating both MOPITT (Measurements Of Pollution In The Troposphere) and IASI (Infrared Atmospheric Sounding Interferometer) retrieved profiles into the Community Earth System Model (CESM). We used the Data Assimilation Research Testbed ensemble Kalman filter technique, with the full atmospheric chemistry CESM component Community Atmospheric Model with Chemistry. We first discuss the methodology and evaluation of the current data assimilation system with coupled meteorology and chemistry data assimilation. The different capabilities of MOPITT and IASI retrievals are highlighted, with particular attention to instrument vertical sensitivity and coverage and how these impact the analyses. MOPITT and IASI CO retrievals mostly constrain the CO fields close to the main anthropogenic, biogenic, and biomass burning CO sources. In the case of IASI CO assimilation, we also observe constraints on CO far from the sources. During the simulation time period (June and July 2008), CO assimilation of both instruments strongly improves the atmospheric CO state as compared to independent observations, with the higher spatial coverage of IASI providing better results on the global scale. However, the enhanced sensitivity of multispectral MOPITT observations to near surface CO over the main source regions provides synergistic effects at regional scales.

This paper presents a validation of a method to derive the vertical profile of carbon monoxide (CO) from its total column using data assimilation. We choose version 3 of MOPITT CO total columns to validate the proposed method. MOPITT products have the advantage of providing both the vertical profiles and the total columns of CO. Furthermore, this version has been extensively validated by comparison with many independent data sets, and has been used in many scientific studies. The first step of the paper consists in the specification of the observation errors based on the chi-square (χ2) test. The observations have been binned according to three types: over land during daytime, over land during night-time, and over sea. Their respective errors using the χ2 metric have been found to be 8, 11 and 7%. In the second step, the CO total columns, with their specified errors, are used within the assimilation system to estimate the vertical profiles. These are compared to the retrieved profiles of MOPITT V3 at global and regional scales. Generally, the two data sets show similar patterns and good agreement at both scales. Nevertheless, total column analyses slightly overestimate CO concentrations compared to MOPITT observations. The mean bias between both data sets is +15 and +12% at 700 and 250 hPa, respectively. In the third step, the assimilation of total column has been compared to the assimilation of MOPITT vertical profiles. The differences between both analyses are very small. In terms longitude–latitude maps, the mean bias between the two data sets is +6 and +8% at the pressure levels 700 and 200 hPa, respectively. In terms of zonal means, the CO distribution is similar for both analyses, with a mean bias which does not exceed 12%. Finally, the two analyses have been validated using independent observations from the aircraft-based MOZAIC program in terms of vertical profiles over eight airports. Over most airports, both analyses agree well with aircraft profiles. For more than 50% of recorded measurements, the difference between the analyses and MOZAIC does not exceed 5 ppbv (parts per billion by volume).

The behavior of the Extra-tropical Transition Layer (ExTL) in the lowermost stratosphere is investigated using a Chemistry Transport Model (CTM) and analyses derived from assimilation of MLS (Microwave Limb Sounder) O3 and MOPITT (Measurements Of Pollution In The Troposphere) CO data. We use O3-CO correlations to quantify the effect of the assimilation on the height and depth of the ExTL. We firstly focus on a Stratosphere-Troposphere Exchange (STE) case study which occurred on 15 August 2007 over the British Isles (50° N, 10° W). We also extend the study at the global scale for the month of August 2007. For the STE case study, MOPITT CO analyses have the capability to sharpen the ExTL distribution whereas MLS O3 analyses provide a tropospheric expansion of the ExTL distribution with its maximum close to the thermal tropopause. When MLS O3 and MOPITT CO analyses are used together, the ExTL shows more realistic results and matches the thermal tropopause. At global scale, MOPITT CO analyses still show a sharper chemical transition between stratosphere and troposphere than the free model run. MLS O3 analyses move the ExTL toward the troposphere and broaden it. When MLS O3 analyses and MOPITT CO analyses are used together the ExTL matches the thermal tropopause poleward of 50°. This study shows that data assimilation can help overcome the shortcomings associated with a relatively coarse model resolution. The ExTL spread is larger in the Northern Hemisphere than the Southern Hemisphere suggesting that mixing processes are more active in the UTLS in the Northern Hemisphere than in the Southern Hemisphere. This work opens perspectives for studying the seasonal variations of the ExTL at extra-tropical latitudes.

Cerebrospinal fluid (CSF) was taken from 19 children with bacterial meningitis treated with cefotaxime (300 mg/kg of body weight/day) and vancomycin (60 mg/kg/day). Median levels of drugs in CSF were smaller than expected, as follows: 4.4 microg/ml for cefotaxime, 3.2 microg/ml for desacetylcefotaxime, and 1.7 microg/ml for vancomycin. The median CSF bactericidal titer against an intermediately cefotaxime-resistant pneumococcus was 1:4. Our data suggest at least an additive interaction between the drugs used in this study.

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by the COVID-19 lockdown using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI), surface site measurements and simulations from the Copernicus Atmospheric Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of NO2 changes have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite NO2 column changes with both weather-normalized surface NO2 concentration changes and simulated changes by the CAMS regional ensemble, composed of 11 models, using recently published emission reductions induced by the lockdown. We show that all estimates show the same tendency on NO2 reductions. Locations where the lockdown was stricter show stronger reductions and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Regarding average reductions, estimates based on either satellite observations (−23 %) surface stations (−43 %) or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %) pointing out the importance of the variability in time of such estimates. Observation based machine learning estimates show a stronger temporal variability than the model-based estimates.