The consolidated European synthesis of CH₄ and N₂O emissions for the European Union and United Kingdom: 1990–2019

Abstract

Abstract. 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 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 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 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 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. 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 CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 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 CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 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, which is of great importance for CH4 and N2O, 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 CH4 and N2O 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 observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

European CH4 inversions with ICON-ART coupled to CarbonTracker Data Assimilation Shell

“…While the a posteriori anthropogenic emissions in our study are lower, the pattern of the emission increments are comparable to Bergamaschi et al (2022). However, our results for anthropogenic emissions are comparable to the results for Europe from global inversions (Deng et al, 2022;Petrescu et al, 2023), both when these assimilate in-situ measurements or satellite observations. It is noticeable that the inversion strongly corrects the overall European emissions downwards such that the independently determined emissions that are reported to the UNFCCC are better matched.…”

“…The a posteriori anthropogenic emissions in our study are lower than in most other regional inversions (Bergamaschi et al, 2018(Bergamaschi et al, , 2022Petrescu et al, 2023). It is important to emphasize that in the various inversions partially other in-situ observations or satellite measurements were assimilated.…”

“…European CH 4 emissions were also estimated from global inversions operating at lower resolution and often assimilating a smaller set of observations available over Europe compared to the regional systems. Deng et al (2022) and Petrescu et al (2023) compared a number of global inversions presented previously by Saunois et al (2020) with the reported values. While Deng et al (2022) included all Annex 1 countries (with periodic emission reports) and non-Annex 1 countries (with only sporadic reports) worldwide, Petrescu et al (2023) focused on EU27 + UK and also compared regional inversions with the reported values.…”

“…Deng et al (2022) and Petrescu et al (2023) compared a number of global inversions presented previously by Saunois et al (2020) with the reported values. While Deng et al (2022) included all Annex 1 countries (with periodic emission reports) and non-Annex 1 countries (with only sporadic reports) worldwide, Petrescu et al (2023) focused on EU27 + UK and also compared regional inversions with the reported values. The results showed that anthropogenic CH 4 emissions estimated in regional inversions were generally higher than reported emissions while global inversions were mostly lower.…”

“…Inversions for CH 4 have already been made in previous studies. European CH 4 emissions have been estimated and compared to bottom-up values in both regional (Bergamaschi et al, 2018(Bergamaschi et al, , 2022Petrescu et al, 2023) and global (Deng et al, 2022;Petrescu et al, 2023) inversions, using both surface in-situ measurements and satellite observations. Bergamaschi et al (2018) compared a total of 7 inversion models with a regional setup for the period 2006 to 2012, all models using harmonized observations.…”