Addressing uncertainties in top-down estimates of national-scale greenhouse gas emissions across different inversion systems

As part of the current international effort to limit global warming, signatories to the Paris Agreement are required to quantify their greenhouse gas (GHG) emissions. Former Kyoto Annex I countries thus report their emissions  annually to the United Nations Framework Convention on Climate Change (UNFCCC) . This assessment allows countries to evaluate their progress in reducing GHG emissions and their compliance with existing agreements.
The general approach to quantifying GHG emissions at the national level is to use activity data and emission factors  (bottom-up method). An independent  quantification can be achieved with inverse modelling, which makes use of an a priori estimate, atmospheric transport models (ATM), and atmospheric measurements of GHG concentrations (top-down method). However, the accuracy and uncertainty of inverse estimates are highly dependent on several parameters and modelling choices. Consequently, inter-model variability can be significant, potentially limiting the use of this technique in policy-relevant discussions.
A representative quantification of GHG emissions based on inverse modelling requires an in-depth understanding of different inverse model estimates, their uncertainties and model limitations.  An intercomparison of three inverse methods and a suite of sensitivity tests were performed. This exercise considered two fluorinated gases (HFC-143a and PFC-218), which are potent GHGs with very different emission characteristics (diffuse versus point source). Both are covered under the European F-gas regulation. Additionally, HFC-143a is expected to be phased-down under the Kigali Amendment to the Montreal Protocol.
We found that top-down estimates for Central and Western European countries are most sensitive to the ATM used. For gases with localised emission sources, such as PFC-218, the choice of a priori emissions and assigned model-data mismatch uncertainty are particularly relevant. For gases with widely distributed emission sources, such as HFC-143a, the emission estimates are more consistent and less sensitive to modelling choices. This detailed understanding of uncertainties in top-down estimates is then used to inform how inverse modelling can be used to support the reporting of halogenated GHG emissions at the national and European level.