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During the COVID-19 pandemic, the first lockdown period (March-May 2020) has led to an unprecedented reduction in pollutant emissions. For 3⁄4 of the more than 1,100 available monitoring stations in Europe, the average NO2 concentrations decreased by at least 25% (2.7 μg.m-3) compared to the average concentrations recorded during the same period of the previous seven years. The relative reduction was of similar magnitude in both urban and rural areas.We further investigate the spatial distribution of the O3 change. Relative to the seven years average, positive anomalies were observed in northern Europe and negative anomalies in southwestern Europe. However, the level of total oxidant (Ox = O3 + NO2) remained unchanged except in southwestern Europe where it decreased.At the global scale, the ozone concentration increased only in a few NOx-saturated regions. After presenting data from monitoring stations in Europe, we analyze the drivers of the change in surface ozone concentrations using the global Community Earth System Model. We contrast global simulations of the atmospheric composition with and without lockdown adjusted anthropogenic emissions for the COVID-19 period.By comparing the situation in Europe with that of the United States and China, we show that the reduced cloudiness in northern Europe played a significant role by shifting the photochemical partitioning between NO2 and O3 toward more ozone, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants.These results illustrate the complexity of the processes affecting ozone in the troposphere and hence the difficulty of implementing efficient regulations targeting air quality impacts.

We apply the regional chemistry-transport model WRF-Chem to investigate the air pollution characteristics in three Chinese metropolitan areas (Beijing, Shanghai and Guangzhou) and assess the model performance in wintertime (January 2010) and summertime (July 2010) conditions. Three simulations utilizing the HTAPv2, REASv2 and MACCity anthropogenic emission inventories are conducted and compared with satellite and ground-based measurements to assess the sensitivity of the model predictions to anthropogenic emissions. Model-predicted surface meteorological fields are overall in good agreement with surface observations. However, the model tends to overestimate maximum surface wind speed, a feature also found in previous WRF-Chem modelling studies. Large discrepancies between the three emission inventories are found, specifically in the spatial distribution and the magnitude of the emissions (e.g. over 98%, 86% and 85% differences in some regions for carbon monoxide (CO), nitrogen oxide (NO) and organic carbon (OC) emissions, respectively). As a result, we find large differences between the simulations. When compared to satellite and surface observations, the HTAPv2-and REASv2-based simulations better reproduce the magnitude and spatial patterns of CO total columns and NO2 tropospheric columns derived from MOPITTv6 and GOME2 satellite observations, respectively. The simulations satisfactorily reproduce the air pollution characteristics in January 2010 as observed at surface monitoring stations in Beijing and Shanghai. The HTAPv2-based simulation better captures the pollution episode that occurred in Beijing in mid-January 2010, which is found to be related to a combination of different meteorological factors that led to the build-up of pollution. The high photochemical activity and convective turbulence in July 2010, in addition to differences in the emissions used, led to more pronounced differences between the simulations and lower statistical skills in particular for trace gases. This study shows that a considerable part of uncertainties in air quality model predictions for China can be attributed to uncertainties in current anthropogenic emission inventories. It highlights the need for more accurate and highly resolved emissions in order to improve the accuracy of model predictions. However, this study reveals that in some cases, model-observation discrepancies cannot be attributed only to inaccuracies in the emissions. For example, we found that regardless of the adopted emissions, all of the simulations fail to capture the temporal variability of particulate matter in Guangzhou during January 2010, indicating that factors other than emissions have a considerable influence on the atmospheric composition in this region. This work shows that inaccuracies in the predicted meteorological parameters (e.g. wind speed/direction, relative humidity, etc.) significantly affect the model performance, as found for example in the model predictions for Shanghai for July 2010, which show high and abrupt increases of NOx and particulates due to rapid decreases in wind speed in the model.

The worldwide spread of Covid-19 pandemic caused a dramatic cutback of human activities and triggered a large-scale atmospheric composition experiment. This unfortunate situation provides the opportunity to investigate the response of atmospheric composition to the shutdown measures. Our focus will be on China, where the pandemic emerged in January 2020, and thence strict lockdowns were enforced. Substantial, large-scale decreases in pollutants levels over China and subsequent recovery were revealed by spaceborne observations from TROPOMI instrument on board Sentinel-5 Precursor, as well as by in situ measurements. Most published work on this topic relied on observed changes in column abundances of nitrogen dioxide (NO2), a predominantly anthropogenic compound and an important precursor for ozone production and secondary aerosol formation. Our work adds to this picture by studing the evolution of two other satellite-derived compounds, formaldehyde (HCHO) and peroxyacylnitrate (PAN), observed by TROPOMI and IASI, respectively. HCHO is an intermediate product in the chemical processing of volatile organic compounds (VOCs) of anthropogenic and natural origin. PAN is formed in the oxidation of anthropogenic and biogenic VOCs, and constitute the principal tropospheric NOx reservoir, enabling the transport and release of NOx away from the sources. Chemistry-transport simulations of PAN are challenging due to large uncertainties in formation mechanisms and precursor emissions. We will evaluate and analyze the observed variability of NO2, HCHO, and PAN columns using model simulations with the MAGRITTE v1.1 regional CTM run at 0.5ox0.5o resolution over China for 2019 and 2020. The model uses updated anthropogenic emissions from the CONFORM dataset, which takes into account the reductions during the shutdowns based on traffic and other economic activity data.

In order to fight the spread of the global COVID-19 pandemic, most of the world 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 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 is 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 six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20–30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30–50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15–30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC 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 adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/).

We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx, SOx, NMVOC, NH3, CO, PM10, PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1 × 0.1 deg and regional European 0.1 × 0.05 deg grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS program. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production, traffic counts) at national and local levels were collected and combined with existing meteorological-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily, hourly) the resulting profiles are pollutant-dependent, yearly-dependent (i.e. time series from 2010 to 2017) and/or spatially-dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/). For review purposes, ECCAD has set up an anonymous repository where subsets of the CAMS-GLOB-TEMPOv2.1 and CAMS-REG-TEMPOv2.1data can be accessed directly (https://www7.obs-mip.fr/eccad/essd-surf-emis-cams-tempo/).; We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx, SOx, NMVOC, NH3, CO, PM10, PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air 15 traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1x0.1 deg and regional European 0.1x0.05 deg grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS program. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production, traffic counts) at national and local levels were collected and combined with existing meteorological-dependent parametrizations to account for the 20 influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily, hourly) the resulting profiles are pollutant-dependent, yearly-dependent (i.e. time series from 2010 to 2017) and/or spatially-dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when 25 comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.

Formaldehyde (HCHO) observations from satellites have been widely used to constrain volatile organic compound (VOC) emission estimates. The oxidation of anthropogenic organic compounds accounts for only a small fraction(~7%) of the total HCHO column on global average (Stavrakou et al., 2009). Therefore, the use of satellite observations to infer information about anthropogenic VOC emissions is generally very challenging . However, the relative contribution of anthropogenic VOCs in and around metropolitan centers is expected to be significant. In this study, we use HCHO column data retrieved from the OMI sensor between 2005 and 2018, and calculate monthly averages for every city of more than 500,000 inhabitants based on data within 20 km of the city centers. Because of the dependence of the background and especially of the biogenic VOC source on temperature and solar radiation, and because these contributions might be significant even around large cities, it is not possible to directly infer the anthropogenic contribution to the long-term observed HCHO trends based on HCHO data. To remove these non-anthropogenic contributions, we first regress the monthly averaged columns either onto the monthly maximum surface temperature, obtained by ECMWF reanalysis data, or onto the monthly isoprene flux, calculated with the MEGAN-MOHYCAN model (Guenther et al., 2012, Stavrakou et al. 2018). Only cities for which anthropogenic emissions are estimated to exceed biogenic emission by more than a factor of 3 are considered. In this way, positive trends of up to 3% yr-1 are found over many Asian cities, especially in China and in the Indo-gangetic Plain, whereas over European cities, South Africa and South America negative trends up to -2% yr-1 are derived. The deduced trends are compared to the corresponding trends of global bottom-up anthropogenic VOC emission inventories and are found to be in good overall agreement. Model simulations are further needed to quantify the relationship between anthropogenic emission trends and HCHO columns, accounting for the effect of non-anthropogenic emissions and potential changes in the oxidizing capacity.

This study compares recent CO, NO, NMVOC, SO, BC and OC anthropogenic emissions from several state-of-the-art top-down estimates to global and regional bottom-up inventories and projections from fi...

Air-sea heat and freshwater water fluxes in the Mediterranean Sea play a crucial role in dense water formation. Here, we compare estimates of Mediterranean Sea heat and water budgets from a range of observational datasets and discuss the main differences between them. Taking into account the closure hypothesis at the Gibraltar Strait, we have built several observational estimates of water and heat budgets by combination of their different observational components. We provide then three estimates for water budget and one for heat budget that satisfy the closure hypothesis. We then use these observational estimates to assess the ability of an ensemble of ERA40-driven high resolution (25 km) Regional Climate Models (RCMs) from the FP6-EU ENSEMBLES database, to simulate the various components, and net values, of the water and heat budgets. Most of the RCM Mediterranean basin means are within the range spanned by the observational estimates of the different budget components, though in some cases the RCMs have a tendency to overestimate the latent heat flux (or evaporation) with respect to observations. The RCMs do not show significant improvements of the total water budget estimates comparing to ERA40. Moreover, given the large spread found in observational estimates of precipitation over the sea, it is difficult to draw conclusions on the performance of RCM for the freshwater budget and this underlines the need for better precipitation observations. The original ERA40 value for the basin mean net heat flux is −15 W/m2 which is 10 W/m2 less than the value of −5 W/m2 inferred from the transport measurements at Gibraltar Strait. The ensemble of heat budget values estimated from the models show that most of RCMs do not achieve heat budget closure. However, the ensemble mean value for the net heat flux is −7 ± 21 W/m2, which is close to the Gibraltar value, although the spread between the RCMs is large. Since the RCMs are forced by the same boundary conditions (ERA40 and sea surface temperatures) and have the same horizontal resolution and spatial domain, the reason for the large spread must reside in the physical parameterizations. To conclude, improvements are urgently required to physical parameterizations in state-of-the-art regional climate models, to reduce the large spread found in our analysis and to obtain better water and heat budget estimates over the Mediterranean Sea.

International audience; In this study we have evaluated the ability of the CNRM-ARPEGE CLIMATE V4 general circulation model (GCM) to estimate the present-day hydrological budget components [precipitation minus evaporation over the sea (P − E) and fresh water runoff (R)] over the Mediterranean, Black and Caspian sea basins. Three simulations were performed which were exactly identical except for horizontal resolution, allowing for a unique opportunity to isolate and study the effects of resolution on simulating the hydrological components. Model calculated values of runoff and P − E were compared to a variety of data sources and show that the model's performance improves significantly with increased resolution, especially in regions with mountainous terrain. Corresponding future climate simulations (following the IPCC A2 scenario) were also performed and indicate that while resolution does not seem to have a significant effect on the qualitative impacts of future climate change on the hydrologic balance, quantitatively the results vary significantly among the models. These results suggests that high resolution global models, or downscaling models such as RCMs, are necessary in order to assess the magnitude of future changes in the hydrological components of these basins.