Comment on essd-2022-287

<strong class="journal-contentHeaderColor">Abstract.</strong> Knowledge of the spatial distribution of the fluxes of greenhouse gases and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its Global Stocktake. This study provides a consolidated synthesis of CH₄ and N₂O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27+UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results, inverse modelling estimates, and extends the previous period 1990&ndash;2017 to 2020. BU and TD products are compared with European National GHG Inventories (NGHGI) reported by Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. The uncertainties of NGHGIs were evaluated using the standard deviation obtained by varying parameters of inventory calculations, reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates e.g. anthropogenic and natural fluxes, which, in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. <strong>For CH₄ emissions</strong>, over the updated 2015&ndash;2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH₄ yr^&minus;1 (EDGAR v5v6.0, last year 2018) and 18.4 Tg CH₄ yr^&minus;1 (GAINS, 2015), close to the NGHGI estimates of 17.5 &plusmn; 2.1 Tg CH₄ yr^&minus;1. TD inversions estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high resolution regional TD inversions report a mean emission of 34 Tg CH₄ yr^&minus;1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 Tg CH₄ yr^&minus;1 and 24 Tg CH₄ yr^&minus;1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soils emissions from the JSBACH-HIMMELI model, natural rivers, lakes and reservoirs emissions, geological sources and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH₄ yr^&minus;1. <strong>For N₂O emissions</strong>, over the 2015&ndash;2019 period, both BU products (EDGAR v5v6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N₂O yr^&minus;1, close to the NGHGI data (0.8 &plusmn; 55 % Tg N₂O yr^&minus;1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N₂O yr^&minus;1 (excluding TOMCAT which reported no data). The TD and BU comparison method defined in this study can be "operationalized" for future annual updates for the calculation of CH₄ and N₂O budgets at the national and EU27+UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, of great importance for CH₄ and N₂O, which may help identify sector contributions to divergence between prior and posterior estimates at the annual/inter-annual scale. Even if currently comparison between CH₄ and N₂O inversions estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modelling and observations, as well as modelling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emissions inventories for CH₄, N₂O and other GHGs. The referenced datasets related to figures are visualized at <a href="https://doi.org/10.5281/zenodo.6992472" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.6992472</a> (Petrescu et al., 2022).