In order to fight the spread of the global COVID-19 pandemic, most of the world's countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to current global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1×0.1 latitude–longitude degree resolution on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs are provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first 6 months of 2020. Maximum decreases in the total emissions are found in February in eastern China, with an average reduction of 20 %–30 % in NO_x, NMVOCs (non-methane volatile organic compounds) and SO₂ relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20 %–30 % for NO_x, NMVOCs and CO in Europe and North America and larger decreases (30 %–50 %) in South America. In India and African regions, NO_x and NMVOC emissions are reduced on average by 15 %–30 %. For the other species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC (black carbon) are estimated. As discussed in the paper, reductions vary highly across regions and sectors due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid-19 adjustmeNt Factors fOR eMissions) (https://doi.org/10.25326/88; Doumbia et al., 2020). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/, last access: 23 August 2021).

We report on an evaluation of tropospheric ozone and its precursor gases in three atmospheric chemistry versions as implemented in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), referred to as IFS(CB05BASCOE), IFS(MOZART) and IFS(MOCAGE). While the model versions were forced with the same overall meteorology, emissions, transport and deposition schemes, they vary largely in their parameterisations describing atmospheric chemistry, including the organics degradation, heterogeneous chemistry and photolysis, as well as chemical solver. The model results from the three chemistry versions are compared against a range of aircraft field campaigns, surface observations, ozone-sondes and satellite observations, which provides quantification of the overall model uncertainty driven by the chemistry parameterisations. We find that they produce similar patterns and magnitudes for carbon monoxide (CO) and ozone (O₃), as well as a range of non-methane hydrocarbons (NMHCs), with averaged differences for O₃ (CO) within 10 % (20 %) throughout the troposphere. Most of the divergence in the magnitude of CO and NMHCs can be explained by differences in OH concentrations, which can reach up to 50 %, particularly at high latitudes. There are also comparatively large discrepancies between model versions for NO₂, SO₂ and HNO₃, which are strongly influenced by secondary chemical production and loss. Other common biases in CO and NMHCs are mainly attributed to uncertainties in their emissions. This configuration of having various chemistry versions within IFS provides a quantification of uncertainties induced by chemistry modelling in the main CAMS global trace gas products beyond those that are constrained by data assimilation.

Radiative forcing provides an important basis for understanding and predicting global climate changes, but its quantification has historically been done independently for different forcing agents, has involved observations to varying degrees, and studies have not always included a detailed analysis of uncertainties. The Copernicus Atmosphere Monitoring Service reanalysis is an optimal combination of modelling and observations of atmospheric composition. It provides a unique opportunity to rely on observations to quantify the monthly and spatially resolved global distributions of radiative forcing consistently for six of the largest forcing agents: carbon dioxide, methane, tropospheric ozone, stratospheric ozone, aerosol–radiation interactions, and aerosol–cloud interactions. These radiative-forcing estimates account for adjustments in stratospheric temperatures but do not account for rapid adjustments in the troposphere. On a global average and over the period 2003–2017, stratospherically adjusted radiative forcing of carbon dioxide has averaged +1.89 W m⁻² (5 %–95 % confidence interval: 1.50 to 2.29 W m⁻²) relative to 1750 and increased at a rate of 18 % per decade. The corresponding values for methane are +0.46 (0.36 to 0.56) W m⁻² and 4 % per decade but with a clear acceleration since 2007. Ozone radiative-forcing averages +0.32 (0 to 0.64) W m⁻², almost entirely contributed by tropospheric ozone since stratospheric ozone radiative forcing is only +0.003 W m⁻². Aerosol radiative-forcing averages −1.25 (−1.98 to −0.52) W m⁻², with aerosol–radiation interactions contributing −0.56 W m⁻² and aerosol–cloud interactions contributing −0.69 W m⁻² to the global average. Both have been relatively stable since 2003. Taking the six forcing agents together, there is no indication of a sustained slowdown or acceleration in the rate of increase in anthropogenic radiative forcing over the period. These ongoing radiative-forcing estimates will monitor the impact on the Earth's energy budget of the dramatic emission reductions towards net-zero that are needed to limit surface temperature warming to the Paris Agreement temperature targets. Indeed, such impacts should be clearly manifested in radiative forcing before being clear in the temperature record. In addition, this radiative-forcing dataset can provide the input distributions needed by researchers involved in monitoring of climate change, detection and attribution, interannual to decadal prediction, and integrated assessment modelling. The data generated by this work are available at https://doi.org/10.24380/ads.1hj3y896 (Bellouin et al., 2020b).

The Copernicus Atmosphere Monitoring Service (CAMS) provides routine analyses and forecasts of trace gases and aerosols on a global scale. The core is the European Centre for Medium Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where modules for atmospheric chemistry and aerosols have been introduced and which allows for data assimilation of satellite retrievals of composition. We have updated both the homogeneous and heterogeneous NO_x chemistry applied in the three independent tropospheric–stratospheric chemistry modules maintained within CAMS, referred to as IFS(CB05BASCOE), IFS(MOCAGE) and IFS(MOZART). Here we focus on the evaluation of main trace gas products from these modules that are of interest as markers of air quality, namely lower-tropospheric O₃, NO₂ and CO, with a regional focus over the contiguous United States. Evaluation against lower-tropospheric composition reveals overall good performance, with chemically induced biases within 10 ppb across species for regions within the US with respect to a range of observations. The versions show overall equal or better performance than the CAMS reanalysis, which includes data assimilation. Evaluation of surface air quality aspects shows that annual cycles are captured well, albeit with variable seasonal biases. During wintertime conditions there is a large model spread between chemistry schemes in lower-tropospheric O₃ (∼ 10 %–35 %) and, in turn, oxidative capacity related to NO_x lifetime differences. Analysis of differences in the HNO₃ and PAN formation, which act as reservoirs for reactive nitrogen, revealed a general underestimate in PAN formation over polluted regions, likely due to too low organic precursors. Particularly during wintertime, the fraction of NO₂ sequestered into PAN has a variability of 100 % across chemistry modules, indicating the need for further constraints. Notably, a considerable uncertainty in HNO₃ formation associated with wintertime N₂O₅ conversion on wet particle surfaces remains. In summary, this study has indicated that the chemically induced differences in the quality of CAMS forecast products over the United States depends on season, trace gas, altitude and region. While analysis of the three chemistry modules in CAMS provide a strong handle on uncertainties associated with chemistry modeling, the further improvement of operational products additionally requires coordinated development involving emissions handling, chemistry and aerosol modeling, complemented with data-assimilation efforts.

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.

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.