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The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union’s Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF’s Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014–21) and outlines some of its new components for the second phase (2021–28), e.g., the new Copernicus anthropogenic CO2 emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases.

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.

The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km × 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.e. after the TROPOMI algorithm update to version 02.02.00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i.e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia, leading to large amounts of CO being released into the atmosphere. The paper assesses the impact of TROPOMI CO assimilation on selected CO plumes more closely. While the CO column can be well constrained by the assimilation of TROPOMI CO data, and the fit to individual IAGOS CO profiles in the lower and middle troposphere is considerably improved, the TROPOMI CO columns do not provide further constraints on individual plumes that are transported across continents and oceans at altitudes above 500 hPa.

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Concentrations of atmospheric methane (CH4), the second most important greenhouse gas, continue to grow. In recent years this growth rate has increased further (2020: +15.6 ppb), the cause of which remains largely unknown. Here, we demonstrate a high-resolution (∼80 km), short-window (24 h) 4D-Var global inversion system based on the ECMWF Integrated Forecasting System (IFS) and newly available satellite observations. The largest national disagreement found between prior (5.3 Tg per month) and posterior (5.0 Tg per month) CH4 emissions is from China, mainly attributed to the energy sector. Emissions estimated from our global system are in good agreement with those of previous regional studies and point source-specific studies. Emission events (leaks or blowouts) > 10 t CH4 h−1 were detected, but without appropriate prior uncertainty information, were not well quantified. Our results suggest that global anthropogenic CH4 emissions for the first 6 months of 2020 were, on average, 470 Gg per month (+1.6 %) higher than for 2019, mainly attributed to the energy and agricultural sectors. Regionally, the largest increases were seen from China (+220 Gg per month, 4.3 %), with smaller increases from India (+50 Gg per month, 1.5 %) and the USA (+40 Gg per month, 2.2 %). When assuming a consistent year-on-year positive trend in emissions, results show that during the onset of the global slowdown (March–April 2020) energy sector CH4 emissions from China increased above expected levels; however, during later months (May–June 2020) emissions decreased below expected levels. Results for the first 6 months of 2019/20 suggest that the accumulated impact of the COVID-19 slowdown on CH4 emissions from March–June 2020 might be small relative to the long-term positive trend in emissions. Changes in OH concentration, not investigated here, may have contributed to the observed growth in 2020.

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 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. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, Arabian Peninsula and 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, 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.

Global Climate is one chapter from the State of the Climate in 2019 annual report and is avail- able from https://doi.org/10.1175/BAMS-D-20-0104.1 Compiled by NOAA’s National Centers for Environmental Information, State of the Climate in 2019 is based on contributions from scien- tists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instru- ments located on land, water, ice, and in space. The full report is available from https://doi.org/10.1175/2020BAMSStateoftheClimate.1.

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.

The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km x 7 km). Near-real time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmospheric Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Atmospheric Sounding Interferometer (IASI) outside the polar regions. In the global mean, CAMS TCCO analysis values are about 10 % lower than TROPOMI TCCO when only IASI and MOPITT thermal infrared (TIR) TCCO data are assimilated (averaged over the period November 2018 to Dec 2021), with the largest relative differences (11–14 %) found in the polar latitude bands, i.e., the areas where no satellite CO retrievals are assimilated in the global CAMS system. Most of these differences are due to a low TCCO bias of the CAMS model, rather than a high bias of TROPOMI. The assimilation of NRT TROPOMI TCCO data in the CAMS system was tested for the period 2021-07-06 to 2021-12-31, i.e., after the TROPOMI algorithm update to version 02.02.00 in July 2021. It leads to a much-improved CO analysis field, with increased CO values and improved fit to independent observations, such as IAGOS aircraft profiles, NDACC FTIR tropospheric and total column CO data, as well as surface CO data from the Air Now, AirBase and Chinese air quality networks. The largest absolute and relative changes from the assimilation of TROPOMI CO, in addition to the already assimilated IASI and MOPITT TIR TCCO data, are found in the lower and mid troposphere, i.e., that part of the atmosphere that is not already well constrained by the already assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia leading to large amounts of CO being released into the atmosphere. The CAMS CO analysis captures the high CO columns resulting from these fires and also shows plumes of high CO from the boreal wildfires that are transported from Siberia over the North Pole and from North America over the North Atlantic reaching as far as Europe, even though some of the high CO values detected in the upper troposphere by IAGOS aircraft that intersected parts of the plumes are not quite reached in the CAMS CO analysis.

Tropopause folds are the key process underlying stratosphere-to-troposphere transport (STT) of ozone, and thus they affect tropospheric ozone levels and variability. In the present study we perform a process-oriented evaluation of Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (CAMSRA) O3 during folding events over Europe and for the time period from 2003 to 2018. A 3-D labeling algorithm is applied to detect tropopause folds in CAMSRA, while ozonesonde data from WOUDC (World Ozone and Ultraviolet Radiation Data Centre) and aircraft measurements from IAGOS (In-service Aircraft for a Global Observing System) are used for CAMSRA O3 evaluation. The profiles of observed and CAMSRA O3 concentrations indicate that CAMSRA reproduces the observed O3 increases in the troposphere during the examined folding events. Nevertheless, at most of the examined sites, CAMSRA overestimates the observed O3 concentrations, mostly at the upper portion of the observed increases, with a median fractional gross error (FGE) among the examined sites >0.2 above 400 hPa. The use of a control run without data assimilation reveals that the aforementioned overestimation of CAMSRA O3 arises from the data assimilation implementation. Overall, although data assimilation assists CAMSRA O3 to follow the observed O3 enhancements in the troposphere during the STT events, it introduces biases in the upper troposphere resulting in no clear quantitative improvement compared to the control run without data assimilation. Less biased assimilated O3 products, with finer vertical resolution in the troposphere, in addition to higher IFS (Integrated Forecasting System) vertical resolution, are expected to provide a better representation of O3 variability during tropopause folds.

The Copernicus Atmosphere Monitoring Service (CAMS) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) has produced a global reanalysis of aerosol and reactive gases (called CAMSRA) for the period 2003–2016. Space observations of ozone, carbon monoxide, NO2 and aerosol optical depth are assimilated by a 4D-Var method in the 60-layer ECMWF global atmospheric model, which for the reanalysis is operated at a horizontal resolution of about 80 km. As a contribution to the evaluation of the reanalysis, we compare atmospheric concentrations of different reactive species provided by the CAMS reanalysis with independent observational data gathered by airborne instrumentation during the field campaigns INTEX-A, INTEX-B, NEAQS-ITCT, ITOP, AMMA, ARCTAS, VOCALS, YAK-AEROSIB, HIPPO and KORUS-AQ. We show that the reanalysis rather successfully reproduces the observed concentrations of chemical species that are assimilated in the system, including O3 and CO with biases generally less than 20 %, but generally underestimates the concentrations of the primary hydrocarbons and secondary organic species. In some cases, large discrepancies also exist for fast-reacting radicals such as OH and HO2.

The Copernicus Atmosphere Monitoring Service (CAMS) produces operationally global 5-day forecast of atmospheric composition and the weather using ECMWF’s Integrated Forecasting System (IFS) since 2015.Beginning with a system upgrade in June 2018 (45r1), the ozone and aerosol fields have been used in the radiation scheme to account for their radiative impact in the global CAMS forecasts. This approach replaced an aerosol and ozone climatology, which had been used before and which is still used in ECMWF's operational high-resolution medium-range NWP forecasts. The CAMS forecast system, which runs at a resolution of about 40 km, is applied here as a test-bed to explore the importance of aerosol direct feedback in an operational configuration, which can guide developments on composition-weather feedbacks for ECMWF's medium-range, monthly and seasonal forecasts. The CAMS prognostic aerosol simulations and the assimilation of different AOD retrievals (MODIS, VIIRS, S5P) have been substantially further modified in several upgrades of the CAMS operational system since 2018.We will discuss the changes and improvements of temperature forecast errors focusing on the impact of changing the aerosol simulation and AOD assimilation in the recent cycles. These changes were introduced to improve the realism of the aerosol forecasts, which is a key CAMS forecast product, and not developed specifically considering their impact on NWP.We will show NWP scores, evaluation with synop-observations and satellite radiation products to demonstrate the impact of the prognostic aerosols. We will also compare the results of the NWP forecast of the CAMS suite, with the NWP scores of ECMWF high resolution forecast run ay 9 km spatial resolution globally. We will further demonstrate that the consistent updates of both the climatological and prognostic aerosol fields are an important prerequisite for a sound assessment of the importance of prognostic aerosol in NWP applications.

The Copernicus Atmosphere Monitoring Service (CAMS), operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission, provides daily analyses and 5 d forecasts of atmospheric composition, including forecasts of volcanic sulfur dioxide (SO2) in near real time. CAMS currently assimilates total column SO2 products from the GOME-2 instruments on MetOp-B and MetOp-C and the TROPOMI instrument on Sentinel-5P, which give information about the location and strength of volcanic plumes. However, the operational TROPOMI and GOME-2 data do not provide any information about the height of the volcanic plumes, and therefore some prior assumptions need to be made in the CAMS data assimilation system about where to place the resulting SO2 increments in the vertical. In the current operational CAMS configuration, the SO2 increments are placed in the mid-troposphere, around 550 hPa or 5 km. While this gives good results for the majority of volcanic emissions, it will clearly be wrong for eruptions that inject SO2 at very different altitudes, in particular exceptional events where part of the SO2 plume reaches the stratosphere. A new algorithm, developed by the German Aerospace Centre (DLR) for GOME-2 and TROPOMI, optimized in the frame of the ESA-funded Sentinel-5P Innovation–SO2 Layer Height Project, and known as the Full-Physics Inverse Learning Machine (FP_ILM) algorithm, retrieves SO2 layer height from TROPOMI in near real time (NRT) in addition to the SO2 column. CAMS is testing the assimilation of these products, making use of the NRT layer height information to place the SO2 increments at a retrieved altitude. Assimilation tests with the TROPOMI SO2 layer height data for the Raikoke eruption in June 2019 show that the resulting CAMS SO2 plume heights agree better with IASI plume height data than operational CAMS runs without the TROPOMI SO2 layer height information and show that making use of the additional layer height information leads to improved SO2 forecasts. Including the layer height information leads to higher modelled total column SO2 values in better agreement with the satellite observations. However, the plume area and SO2 burden are generally also overestimated in the CAMS analysis when layer height data are used. The main reason for this overestimation is the coarse horizontal resolution used in the minimizations. By assimilating the SO2 layer height data, the CAMS system can predict the overall location of the Raikoke SO2 plume up to 5 d in advance for about 20 d after the initial eruption, which is better than with the operational CAMS configuration (without prior knowledge of the plume height) where the forecast skill is much more reduced for longer forecast lead times.

Volcanic eruptions eject large amounts of ash and trace gases such as sulphur dioxide (SO2) into the atmosphere. A significant difficulty in mitigating the impact of volcanic SO2 clouds on air traffic safety is that these gas emissions can be rapidly transported over long distances. The use of space-borne instruments enables the global monitoring of volcanic SO2 emissions in an economical and risk-free manner. Within the European Space Agency (ESA) Sentinel-5p+ Innovation project, the S5P SO2 Layer Height (S5P+I: SO2 LH) activities led to the improvements on the retrieval algorithm and generation of the corresponding near-real-time S5P SO2 LH products. These are currently operationally provided, in near-real-time, by the German Aerospace Center (DLR) in the framework of the Innovative Products for Analyses of Atmospheric Composition, INPULS, project. The main aim of this paper is to present its extensive verification, accomplished within the S5P+I: SO2 LH project, over major recent volcanic eruptions, against collocated space-born measurements from the IASI/Metop and CALIOP/CALIPSO instruments, as well as assess its impact on the forecasts provided by the Copernicus Atmospheric Monitoring Service, CAMS. The mean difference between S5P and IASI observations for the Raikoke 2019, the Nishinoshima 2020 and the La Soufrière-St Vincent, 2021 eruptive periods is ~0.5 ± 3 km, while for the Taal 2020 eruption, a larger difference was found, between 3 and 4 ± 3 km. The comparison of the daily mean SO2 layer heights further demonstrates the capabilities of this near-real-time product, with slopes between 0.8 and 1 and correlations ranging between 0.6 and 0.8. Comparisons between the S5P+I: SO2 LH and the CALIOP/CALIPSO ash plume height are also satisfactory at −2.5 ± 2 km, considering that the injected SO2 and ash plumes’ locations do not always coincide over an eruption. Furthermore, the CAMS assimilation of the S5P+I: SO2 LH product led to much improved model output against the non-assimilated IASI layer heights, with a mean difference of 1.5 ± 2 km compared to the original CAMS analysis, and improved the geographical spread of the Raikoke volcanic plume following the eruptive days.

The Copernicus Atmosphere Monitoring Service (CAMS), operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission, provides daily analyses and 5-day forecasts of atmospheric composition, including forecasts of volcanic sulphur dioxide (SO2) in near-real time. CAMS currently assimilates total column SO2 retrievals from the GOME-2 instruments on MetOp-B and -C and the TROPOMI instrument on Sentinel-5P which give information about the location and strength of volcanic plumes. However, the operational TROPOMI and GOME-2 retrievals do not provide any information about the height of the volcanic plumes and therefore some prior assumptions need to be made in the CAMS data assimilation system about where to place the resulting SO2 increments in the vertical. In the current operational CAMS configuration, the SO2 increments are placed in the mid-troposphere, around 550 hPa or 5 km. While this gives good results for the majority of volcanic emissions, it will clearly be wrong for eruptions that inject SO2 at very different altitudes, in particular exceptional events where part of the SO2 plume reaches the stratosphere. A new algorithm, developed by DLR for GOME-2 and TROPOMI and optimized in the frame of the ESA-funded Sentinel-5P Innovation–SO2 Layer Height Project, the Full-Physics Inverse Learning Machine (FP_ILM) algorithm, retrieves SO2 layer height from TROPOMI in NRT in addition to the SO2 column. CAMS is testing the assimilation of these data, making use of the NRT layer height information to place the SO2 increments at a retrieved altitude. Assimilation tests with the TROPOMI SO2 layer height data for the Raikoke eruption in June 2019 show that the resulting CAMS SO2 plume heights agree better with IASI plume height retrievals than operational CAMS runs without the TROPOMI SO2 layer height information and that making use of the additional layer height information leads to improved SO2 forecasts than when using the operational CAMS configuration. By assimilating the SO2 layer height data the CAMS system can predict the overall location of the Raikoke SO2 plume up to 5 days in advance for about 20 days after the initial eruption.

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.

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

Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000–2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one‐quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry‐climate model simulations, which assume emissions reductions similar to those caused by the COVID‐19 crisis. COVID‐19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020., Plain Language Summary: Worldwide actions to contain the COVID‐19 virus have closed factories, grounded airplanes, and have generally reduced travel and transportation. Less fuel was burnt, and less exhaust was emitted into the atmosphere. Due to these measures, the concentration of nitrogen oxides and volatile organic compounds (VOCs) decreased in the atmosphere. These substances are important for photochemical production and destruction of ozone in the atmosphere. In clean or mildly polluted air, reducing nitrogen oxides and/or VOCs will reduce the photochemical production of ozone and result in less ozone. In heavily polluted air, in contrast, reducing nitrogen oxides can increase ozone concentrations, because less nitrogen oxide is available to destroy ozone. In this study, we use data from three types of ozone instruments, but mostly from ozonesondes on weather balloons. The sondes fly from the ground up to 30 kilometers altitude. In the first 8 km, we find significantly reduced ozone concentrations in the northern extratropics during spring and summer of 2020, less than in any other year since at least 2000. We suggest that reduced emissions due to the COVID‐19 crisis have lowered photochemical ozone production and have caused the observed ozone reductions in the troposphere., Key Points: In spring and summer 2020, stations in the northern extratropics report on average 7% (4 nmol/mol) less tropospheric ozone than normal Such low tropospheric ozone, over several months, and at so many sites, has not been observed in any previous year since at least 2000 Most of the reduction in tropospheric ozone in 2020 is likely due to emissions reductions related to the COVID‐19 pandemic, NASA | Earth Sciences Division (NASA Earth Science Division) http://dx.doi.org/10.13039/100014573, Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) http://dx.doi.org/10.13039/501100000038, Australian Research Council, Fonds De La Recherche Scientifique ‐ FNRS (FNRS) http://dx.doi.org/10.13039/501100002661, Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659, Bundesministerium für Wirtschaft und Energie (BMWi) http://dx.doi.org/10.13039/501100006360

The Copernicus Atmosphere Monitoring Service (CAMS) is operationally providing forecast and reanalysis products of air quality and atmospheric composition. In this article, we present an extended evaluation of the CAMS global reanalysis data set of four reactive gases, namely, ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO), using multiple independent observations. Our results show that the CAMS model system mostly provides a stable and accurate representation of the global distribution of reactive gases over time. Our findings highlight the crucial impact of satellite data assimilation and emissions, investigated through comparison with a model run without assimilated data. Stratospheric and tropospheric O3 are mostly well constrained by the data assimilation, except over Antarctica after 2012/2013 due to changes in the assimilated data. Challenges remain for O3 in the Tropics and high-latitude regions during winter and spring. At the surface and for short-lived species (NO2), data assimilation is less effective. Total column CO in the CAMS reanalysis is well constrained by the assimilated satellite data. The control run, however, shows large overestimations of total column CO in the Southern Hemisphere and larger year-to-year variability in all regions. Concerning the long-term stability of the CAMS model, we note drifts in the time series of biases for surface O3 and CO in the Northern midlatitudes and Tropics and for NO2 over East Asia, which point to biased emissions. Compared to the previous Monitoring Atmospheric Composition and Climate reanalysis, changes in the CAMS chemistry module and assimilation system helped to reduce biases and enhance the long-term temporal consistency of model results for the CAMS reanalysis.

In this study, we compare the diurnal variation in stratospheric ozone of the MACC (Monitoring Atmospheric Composition and Climate) reanalysis, ECMWF Reanalysis Interim (ERA-Interim), and the free-running WACCM (Whole Atmosphere Community Climate Model). The diurnal variation of stratospheric ozone results from photochemical and dynamical processes depending on altitude, latitude, and season. MACC reanalysis and WACCM use similar chemistry modules and calculate a similar diurnal cycle in ozone when it is caused by a photochemical variation. The results of the two model systems are confirmed by observations of the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) experiment and three selected sites of the Network for Detection of Atmospheric Composition Change (NDACC) at Mauna Loa, Hawaii (tropics), Bern, Switzerland (midlatitudes), and Ny-Ålesund, Svalbard (high latitudes). On the other hand, the ozone product of ERA-Interim shows considerably less diurnal variation due to photochemical variations. The global maxima of diurnal variation occur at high latitudes in summer, e.g., near the Arctic NDACC site at Ny-Ålesund, Svalbard. The local OZORAM radiometer observes this effect in good agreement with MACC reanalysis and WACCM. The sensed diurnal variation at Ny-Ålesund is up to 8% (0.4 ppmv) due to photochemical variations in summer and negligible during the dynamically dominated winter. However, when dynamics play a major role for the diurnal ozone variation as in the lower stratosphere (100–20 hPa), the reanalysis models ERA-Interim and MACC which assimilate data from radiosondes and satellites outperform the free-running WACCM. Such a domain is the Antarctic polar winter where a surprising novel feature of diurnal variation is indicated by MACC reanalysis and ERA-Interim at the edge of the polar vortex. This effect accounts for up to 8% (0.4 ppmv) in both model systems. In summary, MACC reanalysis provides a global description of the diurnal variation of stratospheric ozone caused by dynamics and photochemical variations. This is of high interest for ozone trend analysis and other research which is based on merged satellite data or measurements at different local time.

Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000 to 2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry-climate model simulations, which assume emissions reductions similar to those caused by the COVID-19 crisis. COVID-19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020.

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

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

A reanalysis dataset produced by the Copernicus Atmosphere Monitoring service (CAMS reanalysis, 2003 - present day) augmented by ERA5 data for the years before 2003 is used to describe the evolutio...

Global tropospheric ozone reanalyses constructed using different state-of-the-art satellite data assimilation systems, prepared as part of the Copernicus Atmosphere Monitoring Service (CAMS-iRean and CAMS-Rean) as well as two fully independent reanalyses (TCR-1 and TCR-2, Tropospheric Chemistry Reanalysis), have been intercompared and evaluated for the past decade. The updated reanalyses (CAMS-Rean and TCR-2) generally show substantially improved agreements with independent ground and ozone-sonde observations over their predecessor versions (CAMS-iRean and TCR-1) for diurnal, synoptical, seasonal, and interannual variabilities. For instance, for the Northern Hemisphere (NH) mid-latitudes the tropospheric ozone columns (surface to 300 hPa) from the updated reanalyses show mean biases to within 0.8 DU (Dobson units, 3 % relative to the observed column) with respect to the ozone-sonde observations. The improved performance can likely be attributed to a mixture of various upgrades, such as revisions in the chemical data assimilation, including the assimilated measurements, and the forecast model performance. The updated chemical reanalyses agree well with each other for most cases, which highlights the usefulness of the current chemical reanalyses in a variety of studies. Meanwhile, significant temporal changes in the reanalysis quality in all the systems can be attributed to discontinuities in the observing systems. To improve the temporal consistency, a careful assessment of changes in the assimilation configuration, such as a detailed assessment of biases between various retrieval products, is needed. Our comparison suggests that improving the observational constraints, including the continued development of satellite observing systems, together with the optimization of model parameterizations such as deposition and chemical reactions, will lead to increasingly consistent long-term reanalyses in the future.

Within the framework of the Copernicus Atmosphere Monitoring Service (CAMS) element CAMS-84 (Global and regional a posteriori evaluation and quality assurance), we analyze and evaluate the performance of CAMS forecast systems during the passage of ex-hurricane Ophelia in mid-October 2017, carrying Saharan dust and Iberian fire smoke over several Western European regions. To this end, day-1 forecasts from CAMS-global (ECMWF Integrated Forecast System; IFS) and CAMS-regional (ensemble of seven regional air quality models) products are compared against satellite retrievals (MODIS/Terra and Aqua, CALIPSO) and ground-based measurements. The analysis indicates that dust and smoke are injected into the warm sector of Ophelia, lying in the vicinity of the warm and cold front, respectively, gradually affecting the air quality and atmospheric composition over France, the Netherlands and Great Britain. The distinct pattern of enhanced aerosol optical depth (AOD) over Western coastal Europe seen in satellite retrievals is well reproduced by the CAMS near-real time forecast. The observed implications for air quality (PM10 and PM2.5) are satisfactorily forecasted in qualitative terms by both CAMS-global and CAMS-regional systems, while in quantitative terms, the CAMS-regional system exhibits a better performance in predicting surface PM concentrations (higher correlation and lower bias) compared to the global.

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

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

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

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

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

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

Global tropospheric ozone reanalyses constructed using different state-of-the-art satellite data assimilation systems, prepared as part of the Copernicus Atmosphere Monitoring Service (CAMS-iRean and CAMS-Rean) as well as two fully independent Tropospheric Chemistry Reanalyses (TCR-1 and TCR-2), have been inter-compared and evaluated for the past decade. The updated reanalyses (CAMS-Rean and TCR-2) generally show substantially improved agreements with independent ground and ozonesonde observations over their predecessor versions (CAMS-iRean and TCR-1) for the diurnal, synoptical, seasonal, and decadal variability. The improved performance can be attributed to a mixture of various upgrades, such as revisions in the chemical data assimilation, including the assimilated measurements, and the forecast model performance. The updated chemical reanalyses agree well with each other for most cases, which highlights the usefulness of the current chemical reanalyses in a variety of studies. Meanwhile, significant temporal changes in the reanalysis quality in all the systems can be attributed to discontinuities in the observing systems. To improve the temporal consistency, a careful assessment of changes in the assimilation configuration, such as a detailed assessment of biases between various retrieval products, is needed. Even though the assimilation of multi-species data influences the representation of the trace gases in all the systems and also the precursors’ emissions in the TCR reanalyses, the influence of persistent model errors remains a concern, especially for the lower troposphere. Our comparison suggests that improving the observational constraints, including the continued development of satellite observing systems, together with the optimization of model parameterisations, such as deposition and chemical reactions, will lead to increasingly consistent long-term reanalyses in the future.