Abstract. 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 1990–2017 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. For CH₄ 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 CH₄ yr⁻¹ (EDGAR v5v6.0, last year 2018) and 18.4 Tg CH₄ yr⁻¹ (GAINS, 2015), close to the NGHGI estimates of 17.5 ± 2.1 Tg CH₄ yr⁻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⁻¹. Coarser-resolution global-scale TD inversions result in emission estimates of 23 Tg CH₄ yr⁻¹ and 24 Tg CH₄ yr⁻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⁻¹. For N₂O emissions, over the 2015–2019 period, both BU products (EDGAR v5v6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N₂O yr⁻¹, close to the NGHGI data (0.8 ± 55 % Tg N₂O yr⁻¹). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N₂O yr⁻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 https://doi.org/10.5281/zenodo.6992472 (Petrescu et al., 2022).

中文翻译：

---

欧盟 27 国和英国 CH4 和 N2O 排放的欧洲综合综合：1990-2020 年

摘要。了解温室气体通量的空间分布及其时间变异性以及通量归因于自然和人为过程对于监测《巴黎协定》下减少人为排放的进展并为其全球盘点提供信息至关重要。_{本研究提供了 CH 4}和 N ₂的综合合成欧盟和英国 (EU27+UK) 使用自下而上 (BU) 和自上而下 (TD) 方法的 O 排放，并更新了早期的合成方法 (Petrescu et al., 2020, 2021)。该工作整合了更新的排放清单数据、基于过程的模型结果、数据驱动的部门模型结果、逆向建模估计，并将之前的 1990-2017 年延长至 2020 年。将 BU 和 TD 产品与欧洲国家温室气体清单 (NGHGI) 进行比较由缔约方在 2021 年根据联合国气候变化框架公约 (UNFCCC) 报告。 NGHGI 的不确定性使用通过不同清单计算参数获得的标准偏差进行评估，该标准偏差由欧盟成员国根据政府间专门委员会的指导方针报告气候变化（IPCC）并通过填补空白程序进行协调。使用其他方法（例如大气反演模型 (TD) 或空间分解清单数据集 (BU)）产生的估计值的差异来自多种来源，包括与参数化相关的模型内不确定性以及模型之间的结构差异。通过将 NGHGI 与其他方法进行比较，所包含的活动是估计值之间偏差的主要来源，例如人为和自然通量，在大气反演中，这对排放的先前地理空间分布很敏感。对于 CH ₄排放量，在更新的 2015-2019 年期间，涵盖了足够可靠的重叠估算数量，最重要的是 NGHGI，人为 BU 方法具有直接可比性，平均排放量为 20.5 Tg CH ₄ yr ^-1 ( EDGAR v5v6.0，去年 2018）和 18.4 Tg CH ₄ yr ^-1（增益，2015），接近 NGHGI 估计的 17.5 ± 2.1 Tg CH ₄ yr ^-1。TD 反演估计给出了更高的排放估计，因为它们也检测到自然排放。在同一时期，高分辨率区域 TD 反演报告的平均排放量为 34 Tg CH ₄ yr ^-1. 更粗略的全球尺度 TD 反演导致 23 Tg CH ₄ yr ^-1和 24 Tg CH ₄ yr ^-1的排放估计值分别从 GOSAT 和地表 (SURF) 网络大气测量推断。JSBACH-HIMMELI 模型的天然泥炭地和矿质土壤排放量、天然河流、湖泊和水库排放量、地质来源和生物质燃烧一起可以解释 NGHGI 和反转之间的差距，并解释 8 Tg CH ₄ yr ^-1。对于 N ₂ O 排放，在 2015-2019 年期间，BU 产品（EDGAR v5v6.0 和 GAINS）报告的人为排放平均值为 0.9 Tg N ₂O yr ^-1，接近 NGHGI 数据 (0.8 ± 55 % Tg N ₂ O yr ^-1 )。同期，TD 全球和区域反演的平均值为 1.4 Tg N ₂ O yr ^-1（不包括没有报告数据的 TOMCAT）。本研究中定义的 TD 和 BU 比较方法可用于未来年度更新，以计算国家和 EU27+UK 规模的 CH ₄和 N ₂ O 预算。未来的可比性将通过进一步的步骤得到加强，包括以更精细的时间分辨率进行分析和估计年内时间尺度上的排放，这对 CH ₄和 N _{2非常重要}O，这可能有助于确定部门对年度/年度规模上先前和后验估计之间差异的贡献。即使目前 CH ₄和 N ₂ O 反演估计值与 NGHGI 之间的比较由于反演结果的巨大差异而高度不确定，但从大气观测推断的 TD 反演代表了可以比较清单总量的最独立的数据。随着大气建模和观测以及自然通量建模的预期改进，TD 反演可能会成为验证 CH ₄、N _{2排放清单的最强大工具}O 和其他温室气体。与数字相关的参考数据集在 https://doi.org/10.5281/zenodo.6992472（Petrescu 等人，2022 年）中可视化。