DOI: 10.5281/zenodo.3712339
title: CHE_EDGAR-ECMWF_2015

description: The new CHE_EDGAR-ECMWF_2015 dataset with anthropogenic fossil CO<sub>2</sub> emissions and their uncertainties and with a new 7×7 covariance matrix for the atmospheric transport model was compiled and tested. The fossil CO<sub>2 </sub>emissions include all long cycle carbon emissions from human activities, such as fossil fuel combustion, industrial processes (e.g. cement) and products use, but excludes emissions from land-use change and forestry. Human CO<sub>2 </sub>emission inventories were processed into gridded maps to provide an estimate of prior CO<sub>2</sub> emissions, aggregated in 7 main emissions groups: 1) power generation super-emitters and 2) energy production average-emitters, 3) manufacturing, 4) settlements, 5) aviation, 6) other transport at ground level and 7) others, with estimation of their uncertainty and covariance. For the first implementation it is assumed that each emission group is fully correlated with itself and fully uncorrelated with any other group (only diagonal values are non-zero and equal to log-normal variance). A covariance matrix of 7×7 maintains the size for the inversion system to less than 50, which is adequate and computationally affordable. The CHE_EDGAR-ECMWF_2015 represents the 2015 fossil CO<sub>2</sub> emissions prior at 0.1º×0.1º resolution that has been for the first time to our knowledge completed with full uncertainty information with global coverage. Estimation of emission uncertainties is purely based on IPCC (2006) and IPCC-TFI (2019) emission factor and activity data uncertainty values and assumptions – mainly that emissions are fully uncorrelated. Uncertainties related to the spatial distribution (representativeness of the proxy data and their uncertainty) were not assessed in this study, but they can be included by the user on top of the calculated emission uncertainties. All calculations, performed for the year 2015, are documented so that the methodology and algorithms used can be easily adapted for any other year. The dataset can be directly used in inverse modelling, and ensemble data assimilation applications, such as those envisaged within the Copernicus Atmosphere Monitoring Service (CAMS) system. The CHE_EDGAR-ECMWF_2015 dataset consists of: (i) 1 grid-map with yearly anthropogenic CO<sub>2</sub> emission fluxes per each of 7 groups and 1 all groups summed together (total of 8 grid-maps), in kg·m<sup>-2</sup>·s<sup>-1</sup>; (ii) 2 grid-maps with yearly emissions upper and lower uncertainty bounds per each of 7 groups and 1 all groups summed together (total of 16 grid-maps), in %; (iii) 12 grid-maps with monthly anthropogenic CO<sub>2</sub> emission fluxes per each of 7 groups and 1 all groups summed together (total of 96 grid-maps), in kg·m<sup>-2</sup>·s<sup>-1</sup>; (iv) 2 grid-maps with monthly emissions upper and lower uncertainty bounds per each of 12 months and per each of 7 groups and 1 all groups summed together (total of 192 grid-maps), in %; (v) Excel file with listed information per country. The Excel file is organized in spreadsheets by: 1) geographical entities and their statistical infrastructure development levels, 2) emission groups with their prior upper and lower uncertainty bounds per each geographical entities level type and IPCC activities included in each group, 3) yearly and monthly emission budgets (per group and per geographical entity – total), uncertainties (per group and total), contribution of each group to total geographical entities uncertainty in %. For modelling purposes the CO<sub>2</sub> emission distribution is assumed to be log-normal with reported mean, standard deviation and variance (for the covariance matrices). Contribution of representativeness errors to uncertainties and time correlation are neglected in CHE_EDGAR-ECMWF_2015 and will need to be assessed in successive future studies. The estimation of global gridded emissions with their spatially and temporally distributed uncertainties constitute the backbone for atmospheric inversions to estimate anthropogenic emissions from atmospheric concentrations. Dedicated satellite missions (e.g. Copernicus anthropogenic CO<sub>2</sub> monitoring mission CO<sub>2</sub>M) are being planned to monitor anthropogenic emissions from space and substantially reduce emission uncertainties. The developments in the emission uncertainty based on prior knowledge computation presented in this paper is an important preparatory step for an ensemble-based CO<sub>2</sub> Monitoring and Verification System prototype, such as the one developed within the CHE project.
description: Calculated emissions and uncertainties of fossil CO2 have been compared to other data sets based on the country-specific data reported to UNFCCC and on fuel-specific data reported in the energy statistics of IEA. The global values and their uncertainty at a 2σ range for the CHE_EDGAR-ECMWF_2015 dataset show the lowest value of -4.7/+9.6 % or ±7.1 % range (compared to CDIAC ±8.4 %, EDGAR ±9.0 %, GCP ±10.0 %), which is attributed to the methodology, in particular considering that (i) all calculations were done at the country level and then aggregated to global level assuming no correlation following IPCC (2006), and (ii) all calculations were done separately for upper and lower uncertainty bounds to preserve original information with asymmetric confidence intervals for large uncertainties (not required for the Approach 1 described in IPCC (2006)), but not specified for other datasets. At country level the CHE_EDGAR-ECMWF_2015 dataset provide generally larger uncertainty ranges, that are reduced when more detailed information is available to reduce the uncertainties; in summary, using the information that is uniformly available for all countries a coherent uncertainty representation is obtained. The CHE_EDGAR-ECMWF_2015 dataset has been tested to provide the ECMWF Earth system ensemble spread to characterise the CO2 atmospheric concentrations' uncertainties in the prototype of the Copernicus CO2 Monitoring and Verification Support Capacity. Annual and monthly uncertainties have been evaluated in the ECMWF's atmospheric transport model IFS ensemble simulations as well as the sensitivity to the spatial distribution of anthropogenic CO2 emissions. Results show to be rather sensitive to the spatial distribution proxies, and most updated proxies and prior uncertainties are better adapted for data assimilation applications. This needs to be studied in a future research project, the Prototype system for a Copernicus CO2 service (CoCO2), that follows the current CHE research project.