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The Copernicus Atmosphere Monitoring Service (CAMS) provides daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as O3, CO, NO2, HCHO and SO2; aerosol species; and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecasting System (IFS) for numerical weather prediction (NWP) and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. The CAMS system receives regular upgrades, following the upgrades of IFS. The last upgrade, Cy48R1, operational since 27 June 2023, was major with a large number of code changes, both for IFS-COMPO and for NWP. The main IFS-COMPO innovations include the introduction of full stratospheric chemistry; a major update of the emissions; a major update of the aerosol model, including the representation of secondary organic aerosol; several updates of the dust life cycle and optics; updates to the inorganic chemistry in the troposphere; and the assimilation of Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) and TROPOspheric Monitoring Instrument (TROPOMI) CO. The CAMS Cy48R1 upgrade was validated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft, and balloon and satellite observations. In this paper we present the validation results for Cy48R1 by comparing them with the skill of the previous operational system (Cy47R3), with the independent observations as reference, for the period October 2022 to June 2023, during which daily forecasts from both cycles are available. Major improvements in skill are found for the ozone profile in the lower–middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations between 10 and 200 hPa, with larger deviations between 1 and 10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3, NO2 and SO2. The CO column assimilation is now anchored by the Infrared Atmospheric Sounding Interferometer (IASI) instead of the Measurements Of Pollution in The Troposphere (MOPITT) instrument, which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. The results of the 47 comparisons are summarised in a scorecard, which shows that 83 % of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17 % indicate a (slight) degradation. This demonstrates the overall success of this upgrade.

Global monitoring of aerosols is required to analyse the impacts of aerosols on air quality and to understand their role in modulating the climate variability. The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecasts and reanalyses of aerosols using the ECMWF Integrated Forecasting System (IFS), constrained by the assimilation of MODIS and Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). Given the potential end of lifetime of MODIS AOD, implementing new AOD observations in the CAMS operational suite is a priority to ensure the continuity of the CAMS forecast performances. The objective of this work is to test the assimilation of the NOAA VIIRS AOD product from S-NPP and NOAA20 satellites in the IFS model. Simulation experiments assimilating VIIRS on top or in place of MODIS were carried out from June 2021 to November 2021 to evaluate the impacts on the AOD analysis. For maritime aerosol background, the assimilation of VIIRS and the use of VIIRS from NOAA20 as an anchor reduce the analysis AOD values compared to MODIS-based experiments, in which the analysis values were too high due to the positive bias of MODIS/Terra over ocean. Over land, the assimilation of VIIRS induces a large increase in the analysis over biomass burning regions where VIIRS shows larger AOD than MODIS due to differences in the aerosol models and cloud filtering between MODIS and VIIRS retrieval algorithms. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, the Arabian Peninsula and a few places in Asia in the July–August period. The assimilation of VIIRS leads to an overall reduction of the bias in AOD analysis evaluated against AERONET measurements, with the largest bias reduction over Europe and desert and maritime sites.

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

The Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument, launched in October 2017, provides unique observations of atmospheric trace gases at a high resolution of about 5 km, with near-daily global coverage, resolving individual sources like thermal powerplants, industrial complexes, fires, medium-scale towns, roads, and shipping routes. Even though Sentinel-5P (S5P) is a global mission, these datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability based on an ensemble of several European models, available at a resolution of 0.1∘ × 0.1∘. In this paper, we present comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons and present quantitative results in the form of maps for individual days, summer and winter months, and a time series for European subregions and cities between May 2018 and March 2021. The CAMS regional products generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the comparison shows a close agreement between TROPOMI and the CAMS ensemble NO2 tropospheric columns with a relative difference of up to 15 % for most European cities. In winter, however, we find a significant discrepancy in the column amounts over much of Europe, with relative differences up to 50 %. The possible causes for these differences are discussed, focusing on the possible impact of retrieval and modeling errors. Apart from comparisons with the CAMS ensemble, we also present results for comparisons with the individual CAMS models for selected months. Furthermore, we demonstrate the importance of the free tropospheric contribution to the estimation of the tropospheric column and thus include profile information from the CAMS configuration of the ECMWF's (European Centre for Medium-Range Weather Forecasts) global integrated model above 3 km altitude in the comparisons. We also show that replacing the global 1∘ × 1∘ a priori information in the retrieval by the regional 0.1∘ × 0.1∘ resolution profiles of CAMS leads to significant changes in the TROPOMI-retrieved tropospheric column, with typical increases at the emission hotspots up to 30 % and smaller increases or decreases elsewhere. As a spinoff, we present a new TROPOMI NO2 level 2 (L2) data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile by the regional CAMS ensemble profile. This European NO2 product is compared with ground-based remote sensing measurements of six Pandora instruments of the Pandonia Global Network and nine Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product for all except two stations is 5 % to 12 % smaller, owing to a reduction in the multiplicative bias. Compared to the CAMS tropospheric NO2 columns, dispersion and correlation parameters with respect to the standard data are, however, superior.

The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecast and reanalysis of aerosols using the ECMWF Integrated Forecasting System with atmospheric composition extension, constrained by the assimilation of MODIS and the Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). The objective of this work is to evaluate two new near-real-time AOD products to prepare for their assimilation into CAMS, namely the Copernicus AOD (collection 1) from the Sea and Land Surface Temperature Radiometer (SLSTR) on board Sentinel 3-A/B over ocean and the NOAA EPS AOD (v2.r1) from VIIRS on board S-NPP and NOAA20 over both land and ocean. The differences between MODIS (C6.1), PMAp (v2.1), VIIRS (v2.r1), and SLSTR (C1) AOD as well as their departure from the modeled AOD were assessed at the model grid resolution (i.e., level-3) using the 3-month AOD average (December 2019–February 2020 and March–May 2020). VIIRS and MODIS show the best consistency across the products, which is explained by instrument and retrieval algorithm similarities. VIIRS AOD is frequently lower over the ocean background and higher over biomass burning and dust source land regions compared to MODIS. VIIRS shows larger spatial coverage over land and resolves finer spatial structures such as the transport of Australian biomass burning smoke over the Pacific, which can be explained by the use of a heavy aerosol detection test in the retrieval algorithm. Our results confirm the positive offset over ocean (i) between Terra/MODIS and Aqua/MODIS due to the non-corrected radiometric calibration degradation of Terra/MODIS in the Dark Target algorithm and (ii) between SNPP/VIIRS and NOAA20/VIIRS due to the positive bias in the solar reflective bands of SNPP/VIIRS. SLSTR AOD shows much smaller level-3 values than the rest of the products, which is mainly related to differences in spatial representativity at the IFS grid spatial resolution due to the stringent cloud filtering applied to the SLSTR radiances. Finally, the geometry characteristics of the instrument, which drive the range of scattering angles sampled by the instrument, can explain a large part of the differences between retrievals such as the positive offset between PMAp datasets from MetOp-B and MetOp-A.

We present a European dataset of daily sector-, pollutant- and country-dependent emission adjustment factors associated with the COVID-19 mobility restrictions for the year 2020. We considered metrics traditionally used to estimate emissions, such as energy statistics or traffic counts, as well as information derived from new mobility indicators and machine learning techniques. The resulting dataset covers a total of nine emission sectors, including road transport, the energy industry, the manufacturing industry, residential and commercial combustion, aviation, shipping, off-road transport, use of solvents, and fugitive emissions from transportation and distribution of fossil fuels. The dataset was produced to be combined with the Copernicus CAMS-REG_v5.1 2020 business-as-usual (BAU) inventory, which provides high-resolution (0.1∘×0.05∘) emission estimates for 2020 omitting the impact of the COVID-19 restrictions. The combination of both datasets allows quantifying spatially and temporally resolved reductions in primary emissions from both criteria pollutants (NOx, SO2, non-methane volatile organic compounds – NMVOCs, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4), as well as assessing the contribution of each emission sector and European country to the overall emission changes. Estimated overall emission changes in 2020 relative to BAU emissions were as follows: −10.5 % for NOx (−602 kt), −7.8 % (−260.2 Mt) for CO2 from fossil fuels, −4.7 % (−808.5 kt) for CO, −4.6 % (−80 kt) for SO2, −3.3 % (−19.1 Mt) for CO2 from biofuels, −3.0 % (−56.3 kt) for PM10, −2.5 % (−173.3 kt) for NMVOCs, −2.1 % (−24.3 kt) for PM2.5, −0.9 % (−156.1 kt) for CH4 and −0.2 % (−8.6 kt) for NH3. The most pronounced drop in emissions occurred in April (up to −32.8 % on average for NOx) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December were 3 to 4 times lower than those occurred during the spring lockdown, as mobility restrictions were generally softer (e.g. curfews, limited social gatherings). Italy, France, Spain, the United Kingdom and Germany were, together, the largest contributors to the total EU27 + UK (27 member states of the European Union and the UK) absolute emission decreases. At the sectoral level, the largest emission declines were found for aviation (−51 % to −56 %), followed by road transport (−15.5 % to −18.8 %), the latter being the main driver of the estimated reductions for the majority of pollutants. The collection of COVID-19 emission adjustment factors (https://doi.org/10.24380/k966-3957, Guevara et al., 2022) and the CAMS-REG_v5.1 2020 BAU gridded inventory (https://doi.org/10.24380/eptm-kn40, Kuenen et al., 2022b) have been produced in support of air quality modelling studies.

This article describes the Integrated Forecasting System aerosol scheme (IFS-AER) used operationally in the IFS cycle 47R1, which was operated by the European Centre for Medium Range Weather Forecasts (ECMWF) in the framework of the Copernicus Atmospheric Monitoring Services (CAMS). It represents an update of the Rémy et al. (2019) article, which described cycle 45R1 of IFS-AER in detail. Here, we detail only the parameterisations of sources and sinks that have been updated since cycle 45R1, as well as recent changes in the configuration used operationally within CAMS. Compared to cycle 45R1, a greater integration of aerosol and chemistry has been achieved. Primary aerosol sources have been updated, with the implementation of new dust and sea salt aerosol emission schemes. New dry and wet deposition parameterisations have also been implemented. Sulfate production rates are now provided by the global chemistry component of IFS. This paper aims to describe most of the updates that have been implemented since cycle 45R1, not just the ones that are used operationally in cycle 47R1; components that are not used operationally will be clearly flagged. Cycle 47R1 of IFS-AER has been evaluated against a wide range of surface and total column observations. The final simulated products, such as particulate matter (PM) and aerosol optical depth (AOD), generally show a significant improvement in skill scores compared to results obtained with cycle 45R1. Similarly, the simulated surface concentration of sulfate, organic matter and sea salt aerosol are improved by cycle 47R1 compared to cycle 45R1. Some biases persist, such as the surface concentrations of nitrate and organic matter being simulated too high. The new wet and dry deposition schemes that have been implemented into cycle 47R1 have a mostly positive impact on simulated AOD, PM and speciated aerosol surface concentration.

The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986, last access: 5 November 2021). The alert products developed by the EUNADICS-AV EWS, i.e. near-real-time (NRT) observations, email notifications and netCDF (Network Common Data Form) alert data products (called NCAP files), have shown significant interest in using selective detection of natural airborne hazards from polar-orbiting satellites. The combination of several sensors inside a single global system demonstrates the advantage of using a triggered approach to obtain selective detection from observations, which cannot initially discriminate the different aerosol types. Satellite products from hyperspectral ultraviolet–visible (UV–vis) and infrared (IR) sensors (e.g. TROPOMI – TROPOspheric Monitoring Instrument – and IASI – Infrared Atmospheric Sounding Interferometer) and a broadband geostationary imager (Spinning Enhanced Visible and InfraRed Imager; SEVIRI) and retrievals from ground-based networks (e.g. EARLINET – European Aerosol Research Lidar Network, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e.g. selective ash detection, SO2 column and plume height, dust cloud, and smoke from wildfires). A total of 23 different alert products are implemented, using 1 geostationary and 13 polar-orbiting satellite platforms, 3 external existing service, and 2 EU and 2 regional ground-based networks. This allows for the identification and the tracking of extreme events. The EUNADICS-AV EWS has also shown the need to implement a future relay of radiological data (gamma dose rate and radionuclides concentrations in ground-level air) in the case of a nuclear accident. This highlights the interest of operating early warnings with the use of a homogenised dataset. For the four types of airborne hazard, the EUNADICS-AV EWS has demonstrated its capability to provide NRT alert data products to trigger data assimilation and dispersion modelling providing forecasts and inverse modelling for source term estimate. Not all of our alert data products (NCAP files) are publicly disseminated. Access to our alert products is currently restricted to key users (i.e. Volcanic Ash Advisory Centres, national meteorological services, the World Meteorological Organization, governments, volcano observatories and research collaborators), as these are considered pre-decisional products. On the other hand, thanks to the EUNADICS-AV–SACS (Support to Aviation Control Service) web interface (https://sacs.aeronomie.be, last access: 5 November 2021), the main part of the satellite observations used by the EUNADICS-AV EWS is shown in NRT, with public email notification of volcanic emission and delivery of tailored images and NCAP files. All of the ATM stakeholders (e.g. pilots, airlines and passengers) can access these alert products through this free channel.

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

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.

We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21 February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes 5 weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for non-methane volatile organic compounds (NMVOCs), −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOCs), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.

This article describes the IFS-AER aerosol module used operationally in the Integrated Forecasting System (IFS) cycle 45R1, operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) in the framework of the Copernicus Atmospheric Monitoring Services (CAMS). We describe the different parameterizations for aerosol sources, sinks, and its chemical production in IFS-AER, as well as how the aerosols are integrated in the larger atmospheric composition forecasting system. The focus is on the entire 45R1 code base, including some components that are not used operationally, in which case this will be clearly specified. This paper is an update to the Morcrette et al. (2009) article that described aerosol forecasts at the ECMWF using cycle 32R2 of the IFS. Between cycles 32R2 and 45R1, a number of source and sink processes have been reviewed and/or added, notably increasing the complexity of IFS-AER. A greater integration with the tropospheric chemistry scheme of the IFS has been achieved for the sulfur cycle and for nitrate production. Two new species, nitrate and ammonium, have also been included in the forecasting system. Global budgets and aerosol optical depth (AOD) fields are shown, as is an evaluation of the simulated particulate matter (PM) and AOD against observations, showing an increase in skill from cycle 40R2, used in the CAMS interim ReAnalysis (CAMSiRA), to cycle 45R1.

Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high-resolution fluxes that are more diffuse at lower resolutions. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 which cannot be represented in coarser-resolution models. These representativeness errors need to be considered when assimilating in situ data and high-resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO2. For these reasons, the high-resolution CO2 simulations provided by the CAMS in real time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.

An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu, last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

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.

An operational multi-model forecasting system for air quality including nine different chemical transport models has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible website (http://www.marcopolo-panda.eu, last access: 7 December 2018). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an intercomparison of the different forecasts performed during a given period of time (1–15 March 2017) and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.

Asian dust is a seasonal meteorological phenomenon which affects east Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three 1-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to the aerosol load over China. Data used were the Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target and the Deep Blue aerosol optical depth (AOD). In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative of the availability of independent AOD data from two established ground-based networks (AERONET, Aerosol Robotic Network, and CARSNET, China Aerosol Remote Sensing Network), which could be used to evaluate experiments. Particulate matter (PM) data from the China Environmental Protection Agency were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert dust events and to improve the short-range forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better than the unconstrained experiment, generally showing smaller normalized mean bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts into the forecast up to 48 h. The performance of the global model in terms of particulate matter does not show the same degree of skill as the performance in terms of optical depth. Despite this, the global model is able to capture some regional pollution patterns. This indicates that the global model analyses may be used as boundary conditions for regional air quality models at higher resolution, enhancing their performance in situations in which part of the pollution may have originated from large-scale mechanisms. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a role in delivering improved monitoring of Asian dust optical depth.

A new global reanalysis data set of atmospheric composition (AC) for the period 2003–2015 has been produced by the Copernicus Atmosphere Monitoring Service (CAMS). Satellite observations of total column (TC) carbon monoxide (CO) and aerosol optical depth (AOD), as well as several TC and profile observations of ozone, have been assimilated with the Integrated Forecasting System for Composition (C-IFS) of the European Centre for Medium-Range Weather Forecasting. Compared to the previous Monitoring Atmospheric Composition and Climate (MACC) reanalysis (MACCRA), the new CAMS interim reanalysis (CAMSiRA) is of a coarser horizontal resolution of about 110 km, compared to 80 km, but covers a longer period with the intent to be continued to present day. This paper compares CAMSiRA with MACCRA and a control run experiment (CR) without assimilation of AC retrievals. CAMSiRA has smaller biases than the CR with respect to independent observations of CO, AOD and stratospheric ozone. However, ozone at the surface could not be improved by the assimilation because of the strong impact of surface processes such as dry deposition and titration with nitrogen monoxide (NO), which were both unchanged by the assimilation. The assimilation of AOD led to a global reduction of sea salt and desert dust as well as an exaggerated increase in sulfate. Compared to MACCRA, CAMSiRA had smaller biases for AOD, surface CO and TC ozone as well as for upper stratospheric and tropospheric ozone. Finally, the temporal consistency of CAMSiRA was better than the one of MACCRA. This was achieved by using a revised emission data set as well as by applying careful selection and bias correction to the assimilated retrievals. CAMSiRA is therefore better suited than MACCRA for the study of interannual variability, as demonstrated for trends in surface CO.

This paper describes the pre-operational analysis and forecasting system developed during MACC (Monitoring Atmospheric Composition and Climate) and continued in the MACC-II (Monitoring Atmospheric Composition and Climate: Interim Implementation) European projects to provide air quality services for the European continent. This system is based on seven state-of-the art models developed and run in Europe (CHIMERE, EMEP, EURAD-IM, LOTOS-EUROS, MATCH, MOCAGE and SILAM). These models are used to calculate multi-model ensemble products. The paper gives an overall picture of its status at the end of MACC-II (summer 2014) and analyses the performance of the multi-model ensemble. The MACC-II system provides daily 96 h forecasts with hourly outputs of 10 chemical species/aerosols (O3, NO2, SO2, CO, PM10, PM2.5, NO, NH3, total NMVOCs (non-methane volatile organic compounds) and PAN+PAN precursors) over eight vertical levels from the surface to 5 km height. The hourly analysis at the surface is done a posteriori for the past day using a selection of representative air quality data from European monitoring stations. The performance of the system is assessed daily, weekly and every 3 months (seasonally) through statistical indicators calculated using the available representative air quality data from European monitoring stations. Results for a case study show the ability of the ensemble median to forecast regional ozone pollution events. The seasonal performances of the individual models and of the multi-model ensemble have been monitored since September 2009 for ozone, NO2 and PM10. The statistical indicators for ozone in summer 2014 show that the ensemble median gives on average the best performances compared to the seven models. There is very little degradation of the scores with the forecast day but there is a marked diurnal cycle, similarly to the individual models, that can be related partly to the prescribed diurnal variations of anthropogenic emissions in the models. During summer 2014, the diurnal ozone maximum is underestimated by the ensemble median by about 4 μg m−3 on average. Locally, during the studied ozone episodes, the maxima from the ensemble median are often lower than observations by 30–50 μg m−3. Overall, ozone scores are generally good with average values for the normalised indicators of 0.14 for the modified normalised mean bias and of 0.30 for the fractional gross error. Tests have also shown that the ensemble median is robust to reduction of ensemble size by one, that is, if predictions are unavailable from one model. Scores are also discussed for PM10 for winter 2013–1014. There is an underestimation of most models leading the ensemble median to a mean bias of −4.5 μg m−3. The ensemble median fractional gross error is larger for PM10 (~ 0.52) than for ozone and the correlation is lower (~ 0.35 for PM10 and ~ 0.54 for ozone). This is related to a larger spread of the seven model scores for PM10 than for ozone linked to different levels of complexity of aerosol representation in the individual models. In parallel, a scientific analysis of the results of the seven models and of the ensemble is also done over the Mediterranean area because of the specificity of its meteorology and emissions. The system is robust in terms of the production availability. Major efforts have been done in MACC-II towards the operationalisation of all its components. Foreseen developments and research for improving its performances are discussed in the conclusion.

This paper presents the first ensemble modelling experiment in relation to birch pollen in Europe. The seven-model European ensemble of MACC-ENS, tested in trial simulations over the flowering season of 2010, was run through the flowering season of 2013. The simulations have been compared with observations in 11 countries, all members of the European Aeroallergen Network, for both individual models and the ensemble mean and median. It is shown that the models successfully reproduced the timing of the very late season of 2013, generally within a couple of days from the observed start of the season. The end of the season was generally predicted later than observed, by 5 days or more, which is a known feature of the source term used in the study. Absolute pollen concentrations during the season were somewhat underestimated in the southern part of the birch habitat. In the northern part of Europe, a record-low pollen season was strongly overestimated by all models. The median of the multi-model ensemble demonstrated robust performance, successfully eliminating the impact of outliers, which was particularly useful since for most models this was the first experience of pollen forecasting.

A representation of atmospheric chemistry has been included in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new chemistry modules complement the aerosol modules of the IFS for atmospheric composition, which is named C-IFS. C-IFS for chemistry supersedes a coupled system in which chemical transport model (CTM) Model for OZone and Related chemical Tracers 3 was two-way coupled to the IFS (IFS-MOZART). This paper contains a description of the new on-line implementation, an evaluation with observations and a comparison of the performance of C-IFS with MOZART and with a re-analysis of atmospheric composition produced by IFS-MOZART within the Monitoring Atmospheric Composition and Climate (MACC) project. The chemical mechanism of C-IFS is an extended version of the Carbon Bond 2005 (CB05) chemical mechanism as implemented in CTM Transport Model 5 (TM5). CB05 describes tropospheric chemistry with 54 species and 126 reactions. Wet deposition and lightning nitrogen monoxide (NO) emissions are modelled in C-IFS using the detailed input of the IFS physics package. A 1 year simulation by C-IFS, MOZART and the MACC re-analysis is evaluated against ozonesondes, carbon monoxide (CO) aircraft profiles, European surface observations of ozone (O3), CO, sulfur dioxide (SO2) and nitrogen dioxide (NO2) as well as satellite retrievals of CO, tropospheric NO2 and formaldehyde. Anthropogenic emissions from the MACC/CityZen (MACCity) inventory and biomass burning emissions from the Global Fire Assimilation System (GFAS) data set were used in the simulations by both C-IFS and MOZART. C-IFS (CB05) showed an improved performance with respect to MOZART for CO, upper tropospheric O3, and wintertime SO2, and was of a similar accuracy for other evaluated species. C-IFS (CB05) is about 10 times more computationally efficient than IFS-MOZART.

A new global atmospheric carbon dioxide (CO2) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO2 forecasting system is that the land surface, including vegetation CO2 fluxes, is modelled online within the IFS. Other CO2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO2 fluxes also lead to accumulating errors in the CO2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO2 fluxes compared to total optimized fluxes and the atmospheric CO2 compared to observations. The largest biases in the atmospheric CO2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO2 analyses based on the assimilation of CO2 products retrieved from satellite measurements and CO2 in situ observations, as they become available in near-real time. In this way, the accumulation of errors in the atmospheric CO2 forecast will be reduced. Improvements in the CO2 forecast are also expected with the continuous developments in the operational IFS.

The space and time variabilities of methane (CH4) total column and upper tropospheric mixing ratios are analysed above the Mediterranean Basin (MB) as part of the Chemical and Aerosol Mediterranean Experiment (ChArMEx) programme. Since the analysis of the mid-to-upper tropospheric CH4 distribution from spaceborne sensors and model outputs is challenging, we have adopted a climatological approach and have used a wide variety of data sets. We have combined spaceborne measurements from the Thermal And Near infrared Sensor for carbon Observations – Fourier Transform Spectrometer (TANSO-FTS) instrument on the Greenhouse gases Observing SATellite (GOSAT) satellite, the Atmospheric InfraRed Spectrometer (AIRS) on the AURA platform and the Infrared Atmospheric Sounder Interferometer (IASI) instrument aboard the MetOp-A platform with model results from the Chemical Transport Model (CTM) MOCAGE, and the Chemical Climate Models (CCMs) CNRM-AOCCM and LMDz-OR-INCA (according to different emission scenarios). In order to minimize systematic errors in the spaceborne measurements, we have only considered maritime pixels over the MB. The period of interest spans from 2008 to 2011 considering satellite and MOCAGE data and, regarding the CCMs, from 2001 to 2010. Although CH4 is a long-lived tracer with lifetime of ~12 years and is supposed to be well mixed in the troposphere, an east–west gradient in CH4 is observed and modelled in the mid-to-upper troposphere with a maximum in the Western MB in all seasons except in summer when CH4 accumulates above the Eastern MB. The peak-to-peak amplitude of the east–west seasonal variation in CH4 above the MB in the upper troposphere (300 hPa) is weak but almost twice as great in the satellite measurements (~25 ppbv) as in the model data (~15 ppbv). The maximum of CH4 in summer above the eastern MB can be explained by a series of dynamical processes only occurring in summer. The Asian monsoon traps and uplifts high amounts of CH4 to the upper troposphere where they build up. The Asian Monsoon Anticyclone redistributes these elevated CH4 amounts towards North Africa and the Middle East to finally reach and descend in the eastern MB. In the lower troposphere, the CH4 variability is mainly driven by the local sources of emission in the vicinity of the MB.

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).

In this paper, we present performance analyses for a concept geostationary observing system called MAGEAQ (Monitoring the Atmosphere from Geostationary orbit for European Air Quality). The MAGEAQ mission is designed to include a TIR (thermal infrared) spectrometer and a broadband VIS (visible) radiometer; in this work we study only the TIR component (MAGEAQ-TIR). We have produced about 20 days of MAGEAQ-TIR tropospheric ozone pseudo-observations with a full forward and inverse radiative transfer pseudo-observations simulator. We have studied the expected sensitivity of MAGEAQ-TIR and we have found that it is able to provide a full single piece of information for the ozone column from surface to 6 km (about 1.0 DOF (degrees of freedom) and maximum sensitivity at about 3.0 km, on average), as well as a partially independent surface–3 km ozone column (about 0.6 DOF and maximum sensitivity at about 2.5 km, on average). Then, we have compared the tropospheric ozone profiles and the lower (surface–6 km) and lowermost (surface–3 km) tropospheric ozone column pseudo-observations to the target pseudo-reality, produced with the MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) chemistry and transport model. We have found very small to not significant average biases (< 1% in absolute value, for the surface–6 km TOC (tropospheric ozone column), and about −2 to −3 %, for the surface–3 km TOC) and small RMSEs (root mean square errors; about 1.3 DU (5%), for the surface–6 km TOC, and about 1.5 DU (10%), for the surface–3 km TOC). We have tested the performance of MAGEAQ-TIR at some selected small (0.2° × 0.2°) urban and rural locations. We have found that, while the vertical structures of the lower tropospheric ozone pseudo-reality are sometimes missed, MAGEAQ-TIR's lower and lowermost column pseudo-observations follow stunningly good the MOCAGE column pseudo-reality, with correlation coefficients reaching values of 0.9 or higher. Unprecedented retrieval performance for the lowermost tropospheric ozone column is shown. In any case, our MAGEAQ-TIR pseudo-observations are only partially able to replicate the MOCAGE pseudo-reality variability and temporal cycle at the very lowest layers (surface and 1 km altitude), especially at southern European urban locations, where the photochemistry signal is partially missed or shifted at higher altitudes. Temporal artifacts on the daily cycle are sometimes observed. Stratospheric-to-tropospheric exchanges during short time periods (of the order of 1 day) are detected by the MAGEAQ-TIR pseudo-observations.

Practical implementations of chemical OSSEs (Observing System Simulation Experiments) usually rely on approximations of the pseudo-observations by means of a predefined parametrization of the averaging kernels, which describe the sensitivity of the observing system to the target atmospheric species. This is intended to avoid the use of a computationally expensive pseudo-observations simulator, that relies on full radiative transfer calculations. Here we present an investigation on how no, or limited, scene dependent averaging kernels parametrizations may misrepresent the sensitivity of an observing system. We carried out the full radiative transfer calculation for a three-days period over Europe, to produce reference pseudo-observations of lower tropospheric ozone, as they would be observed by a concept geostationary observing system called MAGEAQ (Monitoring the Atmosphere from Geostationary orbit for European Air Quality). The selected spatio-temporal interval is characterised by an ozone pollution event. We then compared our reference with approximated pseudo-observations, following existing simulation exercises made for both the MAGEAQ and GEOstationary Coastal and Air Pollution Events (GEO-CAPE) missions. We found that approximated averaging kernels may fail to replicate the variability of the full radiative transfer calculations. In addition, we found that the approximations substantially overestimate the capability of MAGEAQ to follow the spatio-temporal variations of the lower tropospheric ozone in selected areas, during the mentioned pollution event. We conclude that such approximations may lead to false conclusions if used in an OSSE. Thus, we recommend to use comprehensive scene-dependent approximations of the averaging kernels, in cases where the full radiative transfer is computationally too costly for the OSSE being investigated.

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°.

To evaluate the added value brought by the next generation of IASI (Infrared Atmospheric Sounder Interferometer) instruments to monitor lower tropospheric (LT) ozone, we developed a pseudo-observation simulator, including a direct simulator of thermal infrared spectra and a full inversion scheme to retrieve ozone concentration profiles. We based our simulations on the instrumental configuration of IASI and of an IASI-like instrument, with a factor 2 improvement in terms of spectral resolution and radiometric noise. This scenario, that will be referred to as IASI/2, is one possible configuration of the IASI-NG (New Generation) instrument (the configuration called IASI-NG/IRS2) currently designed by CNES (Centre National d'Études Spatiales). IASI-NG is expected to be launched in the 2020 timeframe as part of the EPS-SG (EUMETSAT Polar System-Second Generation, formerly post-EPS) mission. We produced one month (August 2009) of tropospheric ozone pseudo-observations based on these two instrumental configurations. We compared the pseudo-observations and we found a clear improvement of LT ozone (up to 6 km altitude) pseudo-observations quality for IASI/2. The estimated total error is expected to be more than 35% smaller at 5 km, and 20% smaller for the LT ozone column. The total error on the LT ozone column is, on average, lower than 10% for IASI/2. IASI/2 is expected to have a significantly better vertical sensitivity (monthly average degrees of freedom surface–6 km of 0.70) and to be sensitive at lower altitudes (more than 0.5 km lower than IASI, reaching nearly 3 km). Vertical ozone layers of 4 to 5 km thickness are expected to be resolved by IASI/2, while IASI has a vertical resolution of 6–8 km. According to our analyses, IASI/2 is expected to have the possibility of effectively separate lower from upper tropospheric ozone information even for low sensitivity scenarios. In addition, IASI/2 is expected to be able to better monitor LT ozone patterns at local spatial scale and to monitor abrupt temporal evolutions occurring at timescales of a few days, thus bringing an expected added value with respect to IASI for the monitoring of air quality.

Predicting how European air quality could evolve over the next decades in the context of changing climate requires the use of climate models to produce results that can be averaged in a climatologically and statistically sound manner. This is a very different approach from the one that is generally used for air quality hindcasts for the present period; analysed meteorological fields are used to represent specifically each date and hour. Differences arise both from the fact that a climate model run results in a pure model output, with no influence from observations (which are useful to correct for a range of errors), and that in a "climate" set-up, simulations on a given day, month or even season cannot be related to any specific period of time (but can just be interpreted in a climatological sense). Hence, although an air quality model can be thoroughly validated in a "realistic" set-up using analysed meteorological fields, the question remains of how far its outputs can be interpreted in a "climate" set-up. For this purpose, we focus on Europe and on the current decade using three 5-yr simulations performed with the multiscale chemistry-transport model MOCAGE and use meteorological forcings either from operational meteorological analyses or from climate simulations. We investigate how statistical skill indicators compare in the different simulations, discriminating also the effects of meteorology on atmospheric fields (winds, temperature, humidity, pressure, etc.) and on the dependent emissions and deposition processes (volatile organic compound emissions, deposition velocities, etc.). Our results show in particular how differing boundary layer heights and deposition velocities affect horizontal and vertical distributions of species. When the model is driven by operational analyses, the simulation accurately reproduces the observed values of O3, NOx, SO2 and, with some bias that can be explained by the set-up, PM10. We study how the simulations driven by climate forcings differ, both due to the realism of the forcings (lack of data assimilated and lower resolution) and due to the lack of representation of the actual chronology of events. We conclude that the indicators such as mean bias, mean normalized bias, RMSE and deviation standards can be used to interpret the results with some confidence as well as the health-related indicators such as the number of days of exceedance of regulatory thresholds. These metrics are thus considered to be suitable for the interpretation of simulations of the future evolution of European air quality.

In this article we study the influence of different characteristics of our assimilation system on surface ozone analyses over Europe. Emphasis is placed on the evaluation of the background error covariance matrix (BECM). Data assimilation systems require a BECM in order to obtain an optimal representation of the physical state. A posteriori diagnostics are an efficient way to check the consistency of the used BECM. In this study we derived a diagnostic to estimate the BECM. On the other hand, an increasingly used approach to obtain such a covariance matrix is to estimate it from an ensemble of perturbed assimilation experiments. We applied this method, combined with variational assimilation, while analysing the surface ozone distribution over Europe. We first show that the resulting covariance matrix is strongly time (hourly and seasonally) and space dependent. We then built several configurations of the background error covariance matrix with none, one or two of its components derived from the ensemble estimation. We used each of these configurations to produce surface ozone analyses. All the analyses are compared between themselves and compared to assimilated data or data from independent validation stations. The configurations are very well correlated with the validation stations, but with varying regional and seasonal characteristics. The largest correlation is obtained with the experiments using time- and space-dependent correlation of the background errors. Results show that our assimilation process is efficient in bringing the model assimilations closer to the observations than the direct simulation, but we cannot conclude which BECM configuration is the best. The impact of the background error covariances configuration on four-days forecasts is also studied. Although mostly positive, the impact depends on the season and lasts longer during the winter season.

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.

A detailed 3-D evaluation of an ensemble of five regional Chemistry Transport Models (RCTM) and one global CTM with focus on free tropospheric ozone over Europe is presented. It is performed over a summer period (June to August 2008) in the context of the GEMS-RAQ project. A data set of about 400 vertical ozone profiles from balloon soundings and commercial aircraft at 11 different locations is used for model evaluation, in addition to satellite measurements with the infrared nadir sounder (IASI) showing largest sensitivity to free tropospheric ozone. In the middle troposphere, the four regional models using the same top and boundary conditions from IFS-MOZART exhibit a systematic negative bias with respect to observed profiles of about −20%. Root Mean Square Error (RMSE) values are constantly growing with altitude, from 22% to 32% to 53%, respectively for 0–2 km, 2–8 km and 8–10 km height ranges. Lowest correlation is found in the middle troposphere, with minimum coefficients (R) between 0.2 to 0.45 near 8 km, as compared to 0.7 near the surface and similar values around 10 km. A sensitivity test made with the CHIMERE mode also shows that using hourly instead of monthly chemical boundary conditions generally improves the model skill (i.e. improve RMSE and correlation). Lower tropospheric 0–6 km partial ozone columns derived from IASI show a clear North-South gradient over Europe, which is qualitatively reproduced by the models. Also the temporal variability showing decreasing ozone concentrations in the lower troposphere (0–6 km columns) during summer is well reproduced by models even if systematic bias remains (the value of the bias being also controlled by the type of used boundary conditions). A multi-day case study of a trough with low tropopause was conducted and showed that both IASI and models were able to resolve strong horizontal gradients of middle and upper tropospheric ozone occurring in the vicinity of an upper tropospheric frontal zone.

Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the predicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.

This paper presents a new version of the Météo-France CNRM Chemistry-Climate Model, so-called CNRM-CCM. It includes some fundamental changes from the previous version (CNRM-ACM) which was extensively evaluated in the context of the CCMVal-2 validation activity. The most notable changes concern the radiative code of the GCM, and the inclusion of the detailed stratospheric chemistry of our Chemistry-Transport model MOCAGE on-line within the GCM. A 47-yr transient simulation (1960–2006) is the basis of our analysis. CNRM-CCM generates satisfactory dynamical and chemical fields in the stratosphere. Several shortcomings of CNRM-ACM simulations for CCMVal-2 that resulted from an erroneous representation of the impact of volcanic aerosols as well as from transport deficiencies have been eliminated. Remaining problems concern the upper stratosphere (5 to 1 hPa) where temperatures are too high, and where there are biases in the NO2, N2O5 and O3 mixing ratios. In contrast, temperatures at the tropical tropopause are too cold. These issues are addressed through the implementation of a more accurate radiation scheme at short wavelengths. Despite these problems we show that this new CNRM CCM is a useful tool to study chemistry-climate applications.

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.

This paper describes the capabilities of a nadir thermal infrared (TIR) sensor proposed for deployment onboard a geostationary platform to monitor ozone (O3) and carbon monoxide (CO) for air quality (AQ) purposes. To assess the capabilities of this sensor we perform idealized retrieval studies considering typical atmospheric profiles of O3 and CO over Europe with different instrument configuration (signal to noise ratio, SNR, and spectral sampling interval, SSI) using the KOPRA forward model and the KOPRA-fit retrieval scheme. We then select a configuration, referred to as GEO-TIR, optimized for providing information in the lowermost troposphere (LmT; 0–3 km in height). For the GEO-TIR configuration we obtain ~1.5 degrees of freedom for O3 and ~2 for CO at altitudes between 0 and 15 km. The error budget of GEO-TIR, calculated using the principal contributions to the error (namely, temperature, measurement error, smoothing error) shows that information in the LmT can be achieved by GEO-TIR. We also retrieve analogous profiles from another geostationary infrared instrument with SNR and SSI similar to the Meteosat Third Generation Infrared Sounder (MTG-IRS) which is dedicated to numerical weather prediction, referred to as GEO-TIR2. We quantify the added value of GEO-TIR over GEO-TIR2 for a realistic atmosphere, simulated using the chemistry transport model MOCAGE (MOdèle de Chimie Atmospherique à Grande Echelle). Results show that GEO-TIR is able to capture well the spatial and temporal variability in the LmT for both O3 and CO. These results also provide evidence of the significant added value in the LmT of GEO-TIR compared to GEO-TIR2 by showing GEO-TIR is closer to MOCAGE than GEO-TIR2 for various statistical parameters (correlation, bias, standard deviation).

Vertical profiles of CO taken from the MOZAIC aircraft database are used to globally evaluate the performance of the GEMS/MACC models, including the ECMWF-Integrated Forecasting System (IFS) model coupled to the CTM MOZART-3 with 4DVAR data assimilation for the year 2004. This study provides a unique opportunity to compare the performance of three offline CTMs (MOZART-3, MOCAGE and TM5) driven by the same meteorology as well as one coupled atmosphere/CTM model run with data assimilation, enabling us to assess the potential gain brought by the combination of online transport and the 4DVAR chemical satellite data assimilation. First we present a global analysis of observed CO seasonal averages and interannual variability for the years 2002–2007. Results show that despite the intense boreal forest fires that occurred during the summer in Alaska and Canada, the year 2004 had comparably lower tropospheric CO concentrations. Next we present a validation of CO estimates produced by the MACC models for 2004, including an assessment of their ability to transport pollutants originating from the Alaskan/Canadian wildfires. In general, all the models tend to underestimate CO. The coupled model and the CTMs perform best in Europe and the US where biases range from 0 to -25% in the free troposphere and from 0 to -50% in the surface and boundary layers (BL). Using the 4DVAR technique to assimilate MOPITT V4 CO significantly reduces biases by up to 50% in most regions. However none of the models, even the IFS-MOZART-3 coupled model with assimilation, are able to reproduce well the CO plumes originating from the Alaskan/Canadian wildfires at downwind locations in the eastern US and Europe. Sensitivity tests reveal that deficiencies in the fire emissions inventory and injection height play a role.

This paper presents an evaluation of a new linear parameterization valid for the troposphere and the stratosphere, based on a first order approximation of the carbon monoxide (CO) continuity equation. This linear scheme (hereinafter noted LINCO) has been implemented in the 3-D Chemical Transport Model (CTM) MOCAGE (MOdèle de Chimie Atmospherique Grande Echelle). First, a one and a half years of LINCO simulation has been compared to output obtained from a detailed chemical scheme output. The mean differences between both schemes are about ±25 ppbv (part per billion by volume) or 15% in the troposphere and ±10 ppbv or 100% in the stratosphere. Second, LINCO has been compared to diverse observations from satellite instruments covering the troposphere (Measurements Of Pollution In The Troposphere: MOPITT) and the stratosphere (Microwave Limb Sounder: MLS) and also from aircraft (Measurements of ozone and water vapour by Airbus in-service aircraft: MOZAIC programme) mostly flying in the upper troposphere and lower stratosphere (UTLS). In the troposphere, the LINCO seasonal variations as well as the vertical and horizontal distributions are quite close to MOPITT CO observations. However, a bias of ~−40 ppbv is observed at 700 Pa between LINCO and MOPITT. In the stratosphere, MLS and LINCO present similar large-scale patterns, except over the poles where the CO concentration is underestimated by the model. In the UTLS, LINCO presents small biases less than 2% compared to independent MOZAIC profiles. Third, we assimilated MOPITT CO using a variational 3D-FGAT (First Guess at Appropriate Time) method in conjunction with MOCAGE for a long run of one and a half years. The data assimilation greatly improves the vertical CO distribution in the troposphere from 700 to 350 hPa compared to independent MOZAIC profiles. At 146 hPa, the assimilated CO distribution is also improved compared to MLS observations by reducing the bias up to a factor of 2 in the tropics. This study confirms that the linear scheme is able to simulate reasonably well the CO distribution in the troposphere and in the lower stratosphere. Therefore, the low computing cost of the linear scheme opens new perspectives to make free runs and CO data assimilation runs at high resolution and over periods of several years.

We present a comparison of tropospheric NO2 from OMI measurements to the median of an ensemble of Regional Air Quality (RAQ) models, and an intercomparison of the contributing RAQ models and two global models for the period July 2008–June 2009 over Europe. The model forecasts were produced routinely on a daily basis in the context of the European GEMS ("Global and regional Earth-system (atmosphere) Monitoring using Satellite and in-situ data") project. The tropospheric vertical column of the RAQ ensemble median shows a spatial distribution which agrees well with the OMI NO2 observations, with a correlation r=0.8. This is higher than the correlations from any one of the individual RAQ models, which supports the use of a model ensemble approach for regional air pollution forecasting. The global models show high correlations compared to OMI, but with significantly less spatial detail, due to their coarser resolution. Deviations in the tropospheric NO2 columns of individual RAQ models from the mean were in the range of 20–34% in winter and 40–62% in summer, suggesting that the RAQ ensemble prediction is relatively more uncertain in the summer months. The ensemble median shows a stronger seasonal cycle of NO2 columns than OMI, and the ensemble is on average 50% below the OMI observations in summer, whereas in winter the bias is small. On the other hand the ensemble median shows a somewhat weaker seasonal cycle than NO2 surface observations from the Dutch Air Quality Network, and on average a negative bias of 14%. Full profile information was available for two RAQ models and for the global models. For these models the retrieval averaging kernel was applied. Minor differences are found for area-averaged model columns with and without applying the kernel, which shows that the impact of replacing the a priori profiles by the RAQ model profiles is on average small. However, the contrast between major hotspots and rural areas is stronger for the direct modeled vertical columns than the columns where the averaging kernels are applied, related to a larger relative contribution of the free troposphere and the coarse horizontal resolution in the a priori profiles compared to the RAQ models. In line with validation results reported in the literature, summertime concentrations in the lowermost boundary layer in the a priori profiles from the DOMINO product are significantly larger than the RAQ model concentrations and surface observations over the Netherlands. This affects the profile shape, and contributes to a high bias in OMI tropospheric columns over polluted regions. The global models indicate that the upper troposphere may contribute significantly to the total column and it is important to account for this in comparisons with RAQ models. A combination of upper troposphere model biases, the a priori profile effects and DOMINO product retrieval issues could explain the discrepancy observed between the OMI observations and the ensemble median in summer.

This paper presents a comprehensive characterization of a very deep stratospheric intrusion which occurred over the British Isles on 15 August 2007. The signature of this event is diagnosed using ozonesonde measurements over Lerwick, UK (60.14° N, 1.19° W) and is also well characterized using meteorological analyses from the global operational weather prediction model of Météo-France, ARPEGE. Modelled as well as assimilated fields of both ozone (O3) and carbon monoxide (CO) have been used in order to better document this event. O3 and CO from Aura/MLS and Terra/MOPITT instruments, respectively, are assimilated into the three-dimensional chemical transport model MOCAGE of Météo-France using a variational 3-D-FGAT (First Guess at Appropriate Time) method. The validation of O3 and CO assimilated fields is done using self-consistency diagnostics and by comparison with independent observations such as MOZAIC (O3 and CO), AIRS (CO) and OMI (O3). It particularly shows in the upper troposphere and lower stratosphere region that the assimilated fields are closer to MOZAIC than the free model run. The O3 bias between MOZAIC and the analyses is −11.5 ppbv with a RMS of 22.4 ppbv and a correlation coefficient of 0.93, whereas between MOZAIC and the free model run, the corresponding values are 33 ppbv, 38.5 ppbv and 0.83, respectively. In the same way, for CO, the bias, RMS and correlation coefficient between MOZAIC and the analyses are −3.16 ppbv, 13 ppbv and 0.79, respectively, whereas between MOZAIC and the free model they are 6.3 ppbv, 16.6 ppbv and 0.71, respectively. The paper also presents a demonstration of the capability of O3 and CO assimilated fields to better describe a stratosphere-troposphere exchange (STE) event in comparison with the free run modelled O3 and CO fields. Although the assimilation of MLS data improves the distribution of O3 above the tropopause compared to the free model run, it is not sufficient to reproduce the STE event well. Assimilated MOPITT CO allows a better qualitative description of the stratospheric intrusion event. The MOPITT CO analyses appear more promising than the MLS O3 analyses in terms of their ability to capture a deep STE event. Therefore, the results of this study open the perspectives for using MOPITT CO in the STE studies.

Three global Chemistry Transport Models – MOZART, MOCAGE, and TM5 – as well as MOZART coupled to the IFS meteorological model including assimilation of ozone (O3) and carbon monoxide (CO) satellite column retrievals, have been compared to surface measurements and MOZAIC vertical profiles in the troposphere over Western/Central Europe for summer 2003. The models reproduce the meteorological features and enhancement of pollution during the period 2–14 August, but not fully the ozone and CO mixing ratios measured during that episode. Modified normalised mean biases are around −25% (except ~5% for MOCAGE) in the case of ozone and from −80% to −30% for CO in the boundary layer above Frankfurt. The coupling and assimilation of CO columns from MOPITT overcomes some of the deficiencies in the treatment of transport, chemistry and emissions in MOZART, reducing the negative biases to around 20%. The high reactivity and small dry deposition velocities in MOCAGE seem to be responsible for the overestimation of O3 in this model. Results from sensitivity simulations indicate that an increase of the horizontal resolution to around 1°×1° and potential uncertainties in European anthropogenic emissions or in long-range transport of pollution cannot completely account for the underestimation of CO and O3 found for most models. A process-oriented TM5 sensitivity simulation where soil wetness was reduced results in a decrease in dry deposition fluxes and a subsequent ozone increase larger than the ozone changes due to the previous sensitivity runs. However this latest simulation still underestimates ozone during the heat wave and overestimates it outside that period. Most probably, a combination of the mentioned factors together with underrepresented biogenic emissions in the models, uncertainties in the modelling of vertical/horizontal transport processes in the proximity of the boundary layer as well as limitations of the chemistry schemes are responsible for the underestimation of ozone (overestimation in the case of MOCAGE) and CO found in the models during this extreme pollution event.

The mechanisms of transport on annual, semi-annual and quasi-biennial time scales in the equatorial (10° S–10° N) stratosphere are investigated using the nitrous oxide (N2O) measurements of the space-borne ODIN Sub-Millimetre Radiometer from November 2001 to June 2005, and the simulations of the three-dimensional chemical transport models MOCAGE and SLIMCAT. Both models are forced with analyses from the European Centre for Medium-range Weather Forecast, but the vertical transport is derived either from the forcing analyses by solving the continuity equation (MOCAGE), or from diabatic heating rates using a radiation scheme (SLIMCAT). The N2O variations in the mid-to-upper stratosphere at levels above 32 hPa are generally well captured by the models though significant differences appear with the observations as well as between the models, attributed to the difficulty of capturing correctly the slow upwelling associated with the Brewer-Dobson circulation. However, in the lower stratosphere, below 32 hPa, the observed variations are shown to be mainly seasonal with peak amplitude at 400–450 K (~17.5–19 km), totally missed by the models. The minimum N2O in June, out of phase by two months with the known minimum seasonal upwelling associated with the Brewer-Dobson circulation and moreover amplified over the Western Pacific compared to Africa is incompatible with the seasonal change of upwelling evoked to explain the O3 annual cycle in the same altitude range (Randel et al., 2007). Unless the 1.5 ppbv amplitude of N2O annual cycle in the upper troposphere is totally wrong, the explanation of the observed N2O annual cycle of 15 ppbv in the lower stratosphere requires another mechanism. A possible candidate for that might be the existence of a downward time-averaged mass flux above specific regions, as shown by Sherwood (2000) over Indonesia, required for compensating the energy sink resulting from the deep overshooting of cold and heavy air at high altitude over intense convective areas. But, since global models do currently not capture this subsidence, it must be recognised that a full explanation of the observations cannot be provided for the moment. However, the coincidence of the peak contrast between the Western Pacific and Africa with the maximum overshooting volume in May reported by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar, suggests a strong influence of deep convection on the chemical composition of the tropical lower stratosphere up to 500 K (21 km).

By applying four-dimensional variational data-assimilation (4-D-Var) to a combined ozone and dynamics Numerical Weather Prediction model (NWP), ozone observations generate wind increments through the ozone-dynamics coupling. The dynamical impact of Aura/MLS satellite ozone profiles is investigated using Météo-France operational ARPEGE NWP 4-D-Var assimilation system for a period of 3 months. A data-assimilation procedure has been designed and run on 6-h windows. The procedure includes: (1) 4-D-Var assimilating both ozone and operational NWP standard observations, (2) ARPEGE transporting ozone as a passive-tracer, (3) MOCAGE, the Météo–France chemistry and transport model re-initializing the ARPEGE ozone background at the beginning time of the assimilation window. Using observation minus forecast statistics, it is found that the ozone assimilation reduces the wind bias in the lower stratosphere. Moreover, the Degrees of Freedom for Signal diagnostics show that the MLS data covering the 68.1–31.6 hPa vertical pressure range are the most informative and their information content is nearly of the same order as tropospheric humidity-sensitive radiances. Furthermore, with the help of error variance reduction diagnostics, the ozone contribution to the reduction of the horizontal divergence background-error variance is shown to be better than tropospheric humidity-sensitive radiances.

Sulphate distributions were simulated with a global chemistry transport model. A chemical scheme describing the sulphur cycle and the parameterisations of the main sinks for sulphate aerosols were included in the model. A six-year simulation was conducted from the years 2000 to 2005, driven by the ECMWF operational analyses. Emissions come from an inventory representative of the year 2000. This paper focuses on the analysis of the sulphate sinks and sources over Europe for the entire period of simulation. The Sulphate burden shows a marked annual cycle, which is the result of the annual variations of the aqueous and gaseous chemistry. Regionally, the monthly mean aerosol burden can vary by a factor of 2 from one year to another, because of different weather conditions, driving chemistry, transport and wet deposition of sulphate aerosols. Sulphate ground concentrations, scavenging fluxes and precipitation modelled were compared with observations. The model represents quite well sulphate fields over Europe, but has a general tendency to overestimate sulphate ground concentrations, in particular over Northern Europe. We assume that it is linked to the representation of the scavenging fluxes, which are underestimated. We suggest that uncertainties in modelled precipitation explain only partially the underestimation of the scavenging fluxes in the model.

In this paper we use the total columns of nitrous oxide (N2O) as retrieved from the radiance spectra as measured by the Infrared Atmospheric Sounding Interferometer (IASI) instrument aboard the MetOp-A platform and distributed by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) during the March–May (MAM) 2008 period. Since the total column of N2O reflects concentrations in the middle troposphere, cloud-free columnar N2O measurements are used to assess transport processes in the equatorial band (10° S–10° N). We compare the measured data set with the outputs produced by the 3-D chemical-transport model MOCAGE during the period MAM 2002–2004. To reflect MAM 2008 concentrations, MOCAGE results have been scaled by a factor 1.0125 in order to represent the change in concentration of N2O since 2004. IASI N2O equatorial measurements show a maximum over Africa (4.96×10−3 kg m−2) and a minimum over South America (4.86×10−3 kg m−2) in reasonable agreement with the outputs from MOCAGE despite the fact that emissions of N2O are more intense over America than over Africa. The amplitude of the longitudinal variation of total column N2O along the equatorial band is twice as intense in the measurements (~1.6%) than as in the model calculations (~0.8%), and much greater than the IASI mean random error (0.16–0.33%). A difference between the two data sets is observed above the Western Pacific (110° E–150° E) with a marked minimum in IASI compared to MOCAGE. Recent theoretical studies (Ricaud et al., 2007 and 2009) have shown the potentially important effect of the Walker and the Hadley cells on the tropospheric distribution of N2O in producing a local maximum in N2O above Africa. Based on equatorial total columns of N2O retrieved from IASI, our results are consistent with the fact that Africa is a zone of convergence of airmasses coming from different convective regions whilst Western Pacific behaves more like a divergence zone.

The transport pathways of carbon monoxide (CO) in the African Upper Troposphere (UT) during the West African Monsoon (WAM) is investigated through the assimilation of CO observations by the Aura Microwave Limb Sounder (MLS) in the MOCAGE Chemistry Transport Model (CTM). The assimilation setup, based on a 3-D First Guess at Assimilation Time (3-D-FGAT) variational method is described. Comparisons between the assimilated CO fields and in situ airborne observations from the MOZAIC program between Europe and both Southern Africa and Southeast Asia show an overall good agreement around the lowermost pressure level sampled by MLS (~215 hPa). The 4-D assimilated fields averaged over the month of July 2006 have been used to determine the main dynamical processes responsible for the transport of CO in the African UT. The studied period corresponds to the second AMMA (African Monsoon Multidisciplinary Analyses) aircraft campaign. At 220 hPa, the CO distribution is characterized by a latitudinal maximum around 5° N mostly driven by convective uplift of air masses impacted by biomass burning from Southern Africa, uplifted within the WAM region and vented predominantly southward by the upper branch of the winter hemisphere Hadley cell. Above 150 hPa, the African CO distribution is characterized by a broad maximum over northern Africa. This maximum is mostly controlled by the large scale UT circulation driven by the Asian Summer Monsoon (ASM) and characterized by the Asian Monsoon Anticyclone (AMA) centered at 30° N and the Tropical Easterly Jet (TEJ) on the southern flank of the anticyclone. Asian pollution uplifted to the UT over large region of Southeast Asia is trapped within the AMA and transported by the anticyclonic circulation over Northeast Africa. South of the AMA, the TEJ is responsible for the tranport of CO-enriched air masses from India and Southeast Asia over Africa. Using the high time resolution provided by the 4-D assimilated fields, we give evidence that the variability of the African CO distribution above 150 hPa and north of the WAM region is mainly driven by the synoptic dynamical variability of both the AMA and the TEJ.

The mechanism of troposphere-stratosphere exchange in the tropics was investigated from space-borne observations of the horizontal distributions of tropospheric-origin long-lived species, nitrous oxide (N2O), methane (CH4) and carbon monoxide (CO), from 150 to 70 hPa in March-April-May by the ODIN/Sub-Millimeter Radiometer (SMR), the Upper Atmosphere Research Satellite (UARS)/Halogen Occultation Experiment (HALOE) and the TERRA/Measurements Of Pollution In The Troposphere (MOPITT) instruments in 2002–2004, completed by recent observations of the AURA/Microwave Limb Sounder (MLS) instrument during the same season in 2005. The vertical resolution of the satellite measurements ranges from 2 to 4 km. The analysis has been performed on isentropic surfaces: 400 K (lower stratosphere) for all the species and 360 K (upper troposphere) only for CO. At 400 K (and 360 K for CO), all gases show significant longitudinal variations with peak-to-trough values of ~5–11 ppbv for N2O, 0.07–0.13 ppmv for CH4, and ~10 ppbv for CO (~40 ppbv at 360 K). The maximum amounts are primarily located over Africa and, depending on the species, secondary more or less pronounced maxima are reported above northern South America and South-East Asia. The lower stratosphere over the Western Pacific deep convective region where the outgoing longwave radiation is the lowest, the tropopause the highest and the coldest, appears as a region of minimum concentration of tropospheric trace species. The possible impact on trace gas concentration at the tropopause of the inhomogeneous distribution and intensity of the sources, mostly continental, of the horizontal and vertical transports in the troposphere, and of cross-tropopause transport was explored with the MOCAGE Chemistry Transport Model. In the simulations, significant longitudinal variations were found on the medium-lived CO (2-month lifetime) with peak-to-trough value of ~20 ppbv at 360 K and ~10 ppbv at 400 K, slightly weaker than observations. However, the CH4 (8–10 year lifetime) and N2O (130-year lifetime) longitudinal variations are significantly weaker than observed: peak-to-trough values of ~0.02 ppmv for CH4 and 1–2 ppbv for N2O at 400 K. The large longitudinal contrast of N2O and CH4 concentrations reported by the space-borne instruments at the tropopause and in the lower stratosphere not captured by the model thus requires another explanation. The suggestion is of strong overshooting over land convective regions, particularly Africa, very consistent with the space-borne Tropical Rainfall Measuring Mission (TRMM) radar maximum overshooting features over the same region during the same season. Compared to observations, the MOCAGE model forced by ECMWF analyses is found to ignore these fast local uplifts, but to overestimate the average uniform vertical transport in the UTLS at all longitudes in the tropics.

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.

This paper discusses the highlights of the EU-funded "Assimilation of Envisat data" (ASSET) project, which has involved assimilation of Envisat atmospheric constituent and temperature data into systems based on Numerical Weather Prediction (NWP) models and chemical transport models (CTMs). Envisat was launched in 2002 and is one of the largest Earth Observation (EO) satellites ever built. It carries several sophisticated EO instruments providing insights into chemistry and dynamics of the atmosphere. In this paper we focus on the assimilation of temperature and constituents from Envisat. The overarching theme of the ASSET project has been to bring together experts from all aspects of the data assimilation problem. This has allowed ASSET to address several themes comprehensively: enhancement of NWP analyses by assimilation of research satellite data; studies of the distribution of stratospheric chemical species by assimilation of research satellite data into CTM systems; objective assessment of the quality of ozone analyses; studies of the spatial and temporal evolution of tropospheric pollutants; enhanced retrievals of Envisat data; and data archival and dissemination. Among the results from the ASSET project, many of which are firsts in their field, we can mention: a positive impact on NWP analyses from assimilation of height-resolved stratospheric humidity and temperature data, and assimilation of limb radiances; the extraction of temperature information from the assimilation of chemical species into CTMs; a first intercomparison between ozone assimilation systems; the extraction of information on tropospheric pollution from assimilation of Envisat data; and the large potential of the Envisat MIPAS dataset. This paper discusses these, often novel, developments and results. Finally, achievements of, and recommendations from, the ASSET project are presented.