• Energy Research

AbstractThe dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO2 emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH4 yr−1) of the methane emission growth rate of China (0.87 Tg CH4 yr−1) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

Monthly estimates of global methane emissions from CarbonTracker Europe - CH4 (CTE-CH4). CTE-CH4 is a Bayesian inversion framework based on an ensemble Kalman filter algorithm using the Eulerian global atmospheric transport model TM5. The gridded fluxes are available with a resolution of 1.0x1.0 degrees and in units of kgCH4/m2/month. The gridded flux file contains variables for posterior fluxes from soils (bio_flux_opt) and anthropogenic sources (anth_flux_opt) and the total posterior flux (total_flux_opt). Priors used: Anthropogenic: EDGAR v6, biosphere/wetlands (soils): LPX-Bern DYPTOP v1.4, Ocean: Weber et al. (2019), Biomass burning: GFED v4.1, Termites: VISIT. A more detailed setup of the inversion is documented in Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. Note: Fluxes are optimised at 1.0x1.0 degrees in northern high latitudes (USA, Canada, Europe and Russia), but are also provided here at the same resolution for other regions.

AbstractIn the framework of the RECCAP2 initiative, we present the greenhouse gas (GHG) and carbon (C) budget of Europe. For the decade of the 2010s, we present a bottom‐up (BU) estimate of GHG net‐emissions of 3.9 Pg CO2‐eq. yr−1 (using a global warming potential on a 100 years horizon), which are largely dominated by fossil fuel emissions. In this decade, terrestrial ecosystems acted as a net GHG sink of 0.9 Pg CO2‐eq. yr−1, dominated by a CO2 sink that was partially counterbalanced by net emissions of CH4 and N2O. For CH4 and N2O, we find good agreement between BU and top‐down (TD) estimates from atmospheric inversions. However, our BU land CO2 sink is significantly higher than the TD estimates. We further show that decadal averages of GHG net‐emissions have declined by 1.2 Pg CO2‐eq. yr−1 since the 1990s, mainly due to a reduction in fossil fuel emissions. In addition, based on both data driven BU and TD estimates, we also find that the land CO2 sink has weakened over the past two decades. A large part of the European CO2 and C sinks is located in Northern Europe. At the same time, we find a decreasing trend in sink strength in Scandinavia, which can be attributed to an increase in forest management intensity. These are partly offset by increasing CO2 sinks in parts of Eastern Europe and Northern Spain, attributed in part to land use change. Extensive regions of high CH4 and N2O emissions are mainly attributed to agricultural activities and are found in Belgium, the Netherlands and the southern UK. We further analyzed interannual variability in the GHG budgets. The drought year of 2003 shows the highest net‐emissions of CO2 and of all GHGs combined.

AbstractThe recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.

Methane ( CH 4) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (XCH 4). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4. These differences are linked to the regional CH 4 sources and sinks, and call for further research.

Cet ensemble de données contient toutes les données (au format csv) liées aux chiffres du document soumis par l'ESSD : « The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK : 1990-2017 » Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R.L., Peylin, P., McGrath, M. J., Solazzo, E., Janssens-Maenhout, G, Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D., Tsuruta, A., Winiwarter, W., Patra, P.K., Kuhnert, M., Orregioni, G. D., Crippa, M., Saunois, M., Perugini, L., Markkanen, T., Aalto, T., Groot Zwaaftink, C.D., Yao, Y., Wilson, C., Conchedda, G., Günther, D., Leip, A., Smith, P., Haussaire, J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., et Dolman, A. J. : La synthèse européenne consolidée des émissions de CH4 et de N2O pour l'UE27 et le Royaume-Uni : 1990-2017, Earth Syst. Sci. Data Discuss., essd-2020-367, in review, 2020. Este conjunto de datos contiene todos los datos (en formato csv) vinculados a las cifras del documento presentado por la ESSD: "The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017" Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R.L., Peylin, P., McGrath, M. J., Solazzo, E., Janssens-Maenhout, G, Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D., Tsuruta, A., Winiwarter, W., Patra, P. K., Kuhnert, M., Orregioni, G. D., Crippa, M., Saunois, M., Perugini, L., Markkanen, T., Aalto, T., Groot Zwaaftink, C. D., Yao, Y., Wilson, C., Conchedda, G., Günther, D., Leip, A., Smith, P., Haussaire, J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., y Dolman, A. J.: The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017, Earth Syst. Sci. Data Discuss., essd-2020-367, en revisión, 2020. تحتوي مجموعة البيانات هذه على جميع البيانات (بتنسيق CSV) المرتبطة بالأرقام الواردة في ورقة ESSD المقدمة: "التوليف الأوروبي الموحد لانبعاثات CH4 و N2O للاتحاد الأوروبي 27 والمملكة المتحدة: 1990-2017" بتريسكو، أ. م. ر.، تشيو، ج.، Ciais, ص. طومسون، ر .ل، بيلين، ص. ماكغراث، MJ, سولازو، هـ.، Janssens - Maenhout، ز، Tubiello, F. N., Bergamaschi, ص. برونر، د.، بيترز، جي بي، Höglund - Isaksson، ل.، رينييه، ص. لويرفالد، R., باستفيكن، د.، تسوروتا، أ.، Winiwarter، دبليو، باترا، بي كيه، Kuhnert, م.، Orregioni, جي دي، كريبا، م.، ساونوا، م.، بيروجيني، ل.، Markkanen, T., آلتو، T., جروت زوافتينك، سي. دي.، ياو، Y., ويلسون، ج.، كونشيددا، G., غونتر، د.، ليب، أ.، سميث، ص. هاوسير، ج. م.، Leppänen, أ.، مانينغ، ايه جيه، ماكنورتون، J., بروكمان، ص. ودولمان، إيه جيه: التوليف الأوروبي الموحد لانبعاثات الميثان وأكسيد النيتروز للاتحاد الأوروبي 27 والمملكة المتحدة: 1990-2017، نظام الأرض. Sci. مناقشة البيانات، essd -2020-367، قيد المراجعة، 2020. This dataset contains all data (in csv format) linked to the figures from the ESSD submitted paper: "The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017" Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R.L., Peylin, P., McGrath, M. J., Solazzo, E., Janssens-Maenhout, G, Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D., Tsuruta, A., Winiwarter, W., Patra, P. K., Kuhnert, M., Orregioni, G. D., Crippa, M., Saunois, M., Perugini, L., Markkanen, T., Aalto, T., Groot Zwaaftink, C. D., Yao, Y., Wilson, C., Conchedda, G., Günther, D., Leip, A., Smith, P., Haussaire, J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., and Dolman, A. J.: The consolidated European synthesis of CH4 and N2O emissions for EU27 and UK: 1990-2017, Earth Syst. Sci. Data Discuss., essd-2020-367, in review, 2020.