Comment on essd-2022-287

<strong class="journal-contentHeaderColor">Abstract.</strong> Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) 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 CH<span class="inline-formula">₄</span> and N<span class="inline-formula">₂</span>O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 <span class="inline-formula">+</span> 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 and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations 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. For CH<span class="inline-formula">₄</span> emissions, over the updated 2015–2019 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 CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span> (EDGARv6.0, last year 2018) and 18.4 Tg CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span> (GAINS, last year 2015), close to the NGHGI estimates of <span class="inline-formula">17.5±2.1</span> Tg CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span>. TD inversion 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 CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span>. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span> inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH<span class="inline-formula">₄</span> yr<span class="inline-formula">^−1</span>. For N<span class="inline-formula">₂</span>O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N<span class="inline-formula">₂</span>O yr<span class="inline-formula">^−1</span>, close to the NGHGI data (<span class="inline-formula">0.8±55</span> % Tg N<span class="inline-formula">₂</span>O yr<span class="inline-formula">^−1</span>). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N<span class="inline-formula">₂</span>O yr<span class="inline-formula">^−1</span> (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 CH<span class="inline-formula">₄</span> and N<span class="inline-formula">₂</span>O budgets at the national and EU27 <span class="inline-formula">+</span> UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH<span class="inline-formula">₄</span> and N<span class="inline-formula">₂</span>O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH<span class="inline-formula">₄</span> and N<span class="inline-formula">₂</span>O inversion 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 modeling and<span id="page1199"/> observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH<span class="inline-formula">₄</span>, N<span class="inline-formula">₂</span>O and other GHGs. The referenced datasets related to figures are visualized at <a href="https://doi.org/10.5281/zenodo.7553800">https://doi.org/10.5281/zenodo.7553800</a> (Petrescu et al., 2023).