A top-down approach was applied in order to estimate the atmospheric emissions of the two most important greenhouse gases, i.e. carbon dioxide (CO2) and methane (CH4) in the European domain from 2018 to 2020. For this purpose, in situ atmospheric measurements, distributed over part of the domain of interest, a particle transport model, a first hypothesis emission field and an inversion algorithm were used.
In line with the aims of the World Meteorological Organization, this study intends to promote the development of top-down modeling and foster connections between the scientific world, policymakers and regulators.
Some countries (e.g. Austria, United Kingdom and Australia) have already included emission estimates obtained from reverse modeling in their national reports (National Inventory Reports) for several years, and submitted them every year to the UNFCCC (United Nations Framework Convention on Climate Change). The application potential of these methods is now recognized given the growing ability to systematically and continuously monitor atmospheric concentrations and greenhouse gas emissions.
From an emission field, through the atmospheric transport model, the inversion system generates a simulated concentration time series at the measurement stations. The inversion algorithm optimizes the first-guess flow field, taking into account the associated uncertainty value, and minimizing discrepancies between real and simulated observations. The emission field obtained from this process is called a posteriori field.
Accurate estimates of national greenhouse gas emissions, mainly from the energy sector, are especially useful to understand the effectiveness of the actions taken to achieve the emission reduction objectives that the country must achieve to comply with international climate agreements.
The national emissions obtained with the top-down approach were compared with the national inventory data (bottom-up approach) and with those reported in the EDGARv6.0 database (Emissions Database for Global Atmospheric Research), obtaining a fair agreement with both, considering the uncertainty associated with the estimate. For the estimates obtained at higher spatial resolution, different locations of emission points in correspondence with large urban and industrial areas are highlighted, but also the absence of locations in similar areas.