CAMS-TEMPO: global and European emission temporal profile maps for atmospheric chemistry modelling

Abstract. 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