EAC4 (ECMWF Atmospheric Composition Reanalysis 4) is the fourth generation ECMWF global reanalysis of atmospheric composition. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using a model of the atmosphere based on the laws of physics and chemistry. This principle, called data assimilation, is based on the method used by numerical weather prediction centres and air quality forecasting centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way to allow for the provision of a dataset spanning back more than a decade. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. The assimilation system is able to estimate biases between observations and to sift good-quality data from poor data. The atmosphere model allows for estimates at locations where data coverage is low or for atmospheric pollutants for which no direct observations are available. The provision of estimates at each grid point around the globe for each regular output time, over a long period, always using the same format, makes reanalysis a very convenient and popular dataset to work with. The observing system has changed drastically over time, and although the assimilation system can resolve data holes, the initially much sparser networks will lead to less accurate estimates. For this reason, EAC4 is only available from 2003 onwards. Although the analysis procedure considers chunks of data in a window of 12 hours in one go, EAC4 provides estimates every 3 hours, worldwide. This is made possible by the 4D-Var assimilation method, which takes account of the exact timing of the observations and model evolution within the assimilation window.

including aerosols

Determining concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, a process key for aerosol-cloud interactions (ACI). However, due to sparse coverage of in-situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source, temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN is one of the goals for quantifying ACI processes and achieving uncertainty reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (RA: EAC4) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMS are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatio-temporal quantification of CCN. Therefore, the CCN retrieved from the CAMS aerosol reanalysis succeed the sole use of AOD as a proxy for global CCN. This CCN dataset features CCN concentrations of global coverage for various supersaturations and aerosol species covering the years from 2003 to 2021 with daily frequency and a spatial resolution of 0.75×0.75 degree and 60 vertical levels. Apart from the CAMSRA data, which is available every 3 hours, CCN are currently only computed once a day at 00:00 UTC. The data comprises 3-D fields of total CCN computed for six different supersaturations (s: 0.1, 0.2, 0.4, 0.6, 0.8 and 1 %) and 3-D CCN fields containing aerosol species CCN from sulfate (SO4), hydrophilic black carbon (BCh) and organic matter (OMh) and three size bins of sea salt aerosol (SS) computed for two supersaturations (s: 0.02 % and 0.8 %) comprising additional aerosol information in the lower and upper supersaturation range, respectively. The current choice of data frequency, resolution and variable dependencies such as supersaturation is made regarding general interest and suitability as well as file size, data storage and computational costs. This dataset offers the opportunity to be used for evaluation of general circulation and earth system models as well as in studies of aerosol-cloud interactions.

The file name of the data sets is composed as follows.

project: QUAERERE (Quantifying aerosol-cloud-climate effects by regime) experiment: CCNCAMS (Cloud condensation nuclei derived from the CAMS reanalysis) version: v1 dataset: Total_CCN (total cloud condensation nuclei) and Aerosol_species_CCN (aerosol species cloud condensation nuclei) year: 2003 to 2021 mon: 1 to 12

Acknowledgement: This dataset was generated using Copernicus Atmosphere Monitoring Service information [2003-2021]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. The source data is downloaded from the Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS) (https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview)

The Copernicus Atmosphere Monitoring Service provides the capacity to continuously monitor the composition of the Earth's atmosphere at global and regional scales. The main global near-real-time production system is a data assimilation and forecasting suite providing two 5-day forecasts per day for aerosols and chemical compounds that are part of the chemical scheme. Prior to 2021-07-01 only two parameters were available, 1. Total Aerosol Optical Depth at 550 nm surface 2. Particulate matter d < 25 um surface Note that system:time_start refers to forecast time.

The APExpose_DE dataset is a long-term (2003-2022) dataset providing ambient air pollution metrics at yearly time resolution for NO2, NO, O3, PM10 and PM2.5 at the NUTS-3 spatial resolution level (corresponding to the Landkreis/Kreisfreie Stadt in Germany).

The sources used for the production of the dataset were monitoring data provided by the European Environmental Agency and the CAMS global reanalysis EAC4 . Stations of the types "Traffic" and "Industrial" were left out for being considered unrepresentative to long-term exposure, those of the type "Background" were included. Each station was geo-located within, and each computed yearly value associated to, a NUTS-3 unit. Within each NUTS-3 unit and for each metric, the yearly values per station were averaged in three ways, giving preference to different station sitings, each representing a different scenario: average, urban, remote.

The monitoring data were produced for the NUTS-3 units and the years where monitoring data for a given pollutant is available. In order to complete the dataset for the NUTS-3 units where no monitoring data for a given pollutant is available, the Copernicus Atmospheric Monitoring Service (CAMS) EAC4 reanalysis was used. The yearly-averaged rasters from CAMS were vectorized and scaled to available monitoring data to obtain values for each NUTS-3 units.

As a final step, the Airbase and CAMS derived data were combined to produce the APExpose_DE dataset.

The data comprises 3-D fields of total cloud condensation nuclei numbers computed for six different supersaturations (0.1, 0.2, 0.4, 0.6, 0.8 and 1%). Total ccn are the sum of four aerosol species ccn originating from sea salt (in three size bins), sulfate and hydrophilic black carbon and organic matter. Total ccn is computed using CAMS reanalysis EAC4 aerosol mass mixing ratios.

Variables contained in dataset Total_CCN: ps (surface air pressure), z (surface geopotential), gh (geopotential height), ccn_ss01 (total cloud condensation nuclei at 0.1% supersaturation), ccn_ss02 (total cloud condensation nuclei at 0.2% supersaturation), ccn_ss04 (total cloud condensation nuclei at 0.4% supersaturation), ccn_ss06 (total cloud condensation nuclei at 0.6% supersaturation), ccn_ss08 (total cloud condensation nuclei at 0.8% supersaturation), ccn_ss10 (total cloud condensation nuclei at 1.0% supersaturation)

In this dataset we present metrics related to the exposure to air pollution in Germany for the decade 2010-2019. The sources used for the production of the dataset were Airbase, from the European Environmental Agency (https://www.eea.europa.eu/themes/air/explore-air-pollution-data) and the CAMS (Copernicus Atmosphere Monitoring Service) global reanalysis EAC4 (https://www.ecmwf.int/en/forecasts/dataset/cams-global-reanalysis). Stations of the types "Traffic" and "Industrial" were left out for being considered unrepresentative to long-term exposure, those of the type "Background" were included. Each station was geo-located within, and each computed yearly value associated to, a NUTS-3 unit. Within each NUTS-3 (Nomenclature of Territorial Units for Statistics) unit and for each metric, the yearly values per station were averaged in three ways, giving preference to different station sitings, each representing a different scenario: average, urban, remote. The monitoring data were produced for the NUTS-3 units and the years where monitoring data for a given pollutant is available. In order to complete the dataset for the NUTS-3 units where no monitoring data for a given pollutant is available, the Copernicus Atmospheric Monitoring Service (CAMS) EAC4 reanalysis (https://www.ecmwf.int/en/forecasts/dataset/cams-global-reanalysis) was used. The yearly-averaged rasters from CAMS were vectorized and scaled to available monitoring data to obtain values for each NUTS-3 units. As a final step, the Airbase and CAMS derived data were combined to produce the APExpose_DE dataset. Each record (each line in the file) corresponds to a NUTS-3 unit (identified by its name and its code), and a scenario, for a given year. There are 402 NUTS-3 units in Germany and 3 scenarios were developed, the total number of records in the dataset is 1206 per year, or 12060 for the entire study period. Each record includes a numeric value for each metric considered. The ASCII format of the provided dataset enables a simple access and workup. The NUTS-3 code, provided for each record, enables linking the dataset to other, possibly vectorized, datasets at the NUTS-3 or coarser level.