Quantification des sources de méthane en Sibérie par inversion atmosphérque à la méso-échelle

Anthopogenic and natural methane emissions in Siberia significantly contribute to theglobal methane budget, but the magnitude of these emissions is uncertain (3–11% of globalemissions). To the South, anthropogenic emissions are related to big urban centres. To theNorth, oil and gas extraction in West Siberia is responsible for conspicuous point sources.These regions are also covered by large natural wetlands emitting methane during the snowfreeseason, roughly from May to September. Regional atmospheric inversions at a meso-scaleprovide a mean for improving our knowledge on all emission process. But inversions sufferfrom the uncertainties in the assimilated observations, in the atmospheric transport modeland in the emission magnitude and distribution. I developp a new inversion method based onerror statistic marginalization in order to account for these uncertainties. I test this methodon case study and explore its robustness. I then apply it to Siberia. Using measurements ofmethane atmospheric concentrations gathered at Siberian surface observation sites, I founda regional methane budget in Siberia of 5–28 TgCH4.a−1 (1–5% of global emissions). Thisimplies a reduction of 50% in the uncertainties on the regional budget. With the new method,I also can detect emission patterns at a resolution of a few thousands km2 and emissionvariability at a resolution of 2–4 weeks.Les émissions anthropiques et naturelles de méthane en Sibérie contribuent de manièrenotable, mais mal quantifiée au budget mondial de méthane (3–11% des émissions mondiales).Au Sud de la région, les émissions anthropiques sont liées aux grands centres urbains.Au Nord, l’extraction de gaz et de pétrole en Sibérie occidentale induit d’importantessources anthropiques ponctuelles. Ces régions sont aussi couvertes de vastes zones humidesnaturelles émettant du méthane durant l’été (typiquement de mai à septembre). Nous utilisonsdes inversions atmosphériques régionales à la méso-échelle pour mieux comprendreles contributions de chaque processus dans le budget sibérien. Les inversions souffrent desincertitudes dans les observations, dans la simulation du transport et dans l’amplitude et ladistribution des émissions. Pour prendre en compte ces incertitudes, je développe une nouvelleméthode d’inversion basée sur une marginalisation des statistiques d’erreurs. Je testecette méthode et documente sa robustesse sur un cas test. Je l’applique ensuite à la Sibérie.À l’aide de mesures de concentrations atmosphériques de méthane collectées par des sitesd’observation de surface en Sibérie, j’estime le budget régional de méthane sibérien à 5–28 TgCH4.a−1 (1–5% des émissions mondiales), soit une réduction de 50% des incertitudespar rapport aux précédentes études dans la région. Grâce à cette méthode, je suis de plus enmesure de détecter des structures d’émissions par zones de quelques milliers de km2 et leurvariabilité à une résolution de 2–4 semaines

Disentangling methane and carbon dioxide sources and transport across the Russian Arctic from aircraft measurements

A more accurate characterization of the sources and sinks of methane (CH4) and carbon dioxide (CO2) in the vulnerable Arctic environment is required to better predict climate change. A large-scale aircraft campaign took place in September 2020 focusing on the Siberian Arctic coast. CH4 and CO2 were measured in situ during the campaign and form the core of this study. Measured ozone (O3) and carbon monoxide (CO) are used here as tracers. Median CH4 mixing ratios are fairly higher than the monthly mean hemispheric reference (Mauna Loa, Hawaii, US) with 1890&ndash;1969 ppb vs 1887 ppb respectively, while CO2 mixing ratios from all flights are lower (408.09&ndash;411.50 ppm vs 411.52 ppm). We also report on three case studies. Our analysis suggests that during the campaign the European part of Russia&rsquo;s Arctic and Western Siberia were subject to long-range transport of polluted air masses, while the East was mainly under the influence of local emissions of greenhouse gases. The relative contributions of the main anthropogenic and natural sources of CH4 are simulated using the Lagrangian model FLEXPART in order to identify dominant sources in the boundary layer and in the free troposphere. In western terrestrial flights, air masses composition is influenced by emissions from wetlands and anthropogenic activities (waste management, fossil fuel industry and to a lesser extent the agricultural sector), while in the East, emissions are dominated by freshwaters, wetlands, and the oceans, with a likely contribution from anthropogenic sources related to fossil fuels. Our results highlight the importance of the contributions from freshwater and oceans emissions. Considering the large uncertainties associated to them, our study suggests that the emissions from these aquatic sources should receive more attention in Siberia.</p

Influences of hydroxyl radicals (OH) on top-down estimates of the global and regional methane budgets

The hydroxyl radical (OH), which is the dominant sink of methane (CH4), plays a key role in closing the global methane budget. Current top-down estimates of the global and regional CH4 budget using 3D models usually apply prescribed OH fields and attribute model–observation mismatches almost exclusively to CH4 emissions, leaving the uncertainties due to prescribed OH fields less quantified. Here, using a variational Bayesian inversion framework and the 3D chemical transport model LMDz, combined with 10 different OH fields derived from chemistry–climate models (Chemistry–Climate Model Initiative, or CCMI, experiment), we evaluate the influence of OH burden, spatial distribution, and temporal variations on the global and regional CH4 budget. The global tropospheric mean CH4-reaction-weighted [OH] ([OH]GM−CH4) ranges 10.3–16.3×105 molec cm−3 across 10 OH fields during the early 2000s, resulting in inversion-based global CH4 emissions between 518 and 757  Tg yr−1. The uncertainties in CH4 inversions induced by the different OH fields are similar to the CH4 emission range estimated by previous bottom-up syntheses and larger than the range reported by the top-down studies. The uncertainties in emissions induced by OH are largest over South America, corresponding to large inter-model differences of [OH] in this region. From the early to the late 2000s, the optimized CH4 emissions increased by 22±6  Tg yr−1 (17–30  Tg yr−1), of which ∼25  % (on average) offsets the 0.7  % (on average) increase in OH burden. If the CCMI models represent the OH trend properly over the 2000s, our results show that a higher increasing trend of CH4 emissions is needed to match the CH4 observations compared to the CH4 emission trend derived using constant OH. This study strengthens the importance of reaching a better representation of OH burden and of OH spatial and temporal distributions to reduce the uncertainties in the global and regional CH4 budgets

Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period

The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolving discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields was analyzed and aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz (Laboratoire de Meteorologie Dynamique) with a standard CH4 emission scenario over the period 2000–2016. The multi-model simulated global volume-weighted tropospheric mean OH concentration ([OH]) averaged over 2000–2010 ranges between 8:7*10^5 and 12:8*10^5 molec cm-3. The inter-model differences in tropospheric OH burden and vertical distributions are mainly determined by the differences in the nitrogen oxide (NO) distributions, while the spatial discrepancies between OH fields are mostly due to differences in natural emissions and volatile organic compound (VOC) chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0:3*10^5 molec cm-3 in the tropospheric mean [OH], with year-to-year variations much smaller than during the historical period 1960–2000. Once ingested into the LMDz model, these OH changes translated into a 5 to 15 ppbv reduction in the CH4 mixing ratio in 2010, which represents 7%–20% of the model-simulated CH4 increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a CH4 mixing ratio uncertainty of > 30 ppbv. Over the full 2000–2016 time period, using a common stateof- the-art but nonoptimized emission scenario, the impact of [OH] changes tested here can explain up to 54% of the gap between model simulations and observations. This result emphasizes the importance of better representing OH abundance and variations in CH4 forward simulations and emission optimizations performed by atmospheric inversions

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

Funding Information: We thank Aurélie Paquirissamy, Géraud Moulas and the ARTTIC team for the great managerial support offered during the project. FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. Annual, gap-filled and harmonized NGHGI uncertainty estimates for the EU and its member states were provided by the EU GHG inventory team (European Environment Agency and its European Topic Centre on Climate change mitigation). Most top-down inverse simulations referred to in this paper rely for the derivation of optimized flux fields on observational data provided by surface stations that are part of networks like ICOS (datasets: 10.18160/P7E9-EKEA , Integrated Non-CO Observing System, 2018a, and 10.18160/B3Q6-JKA0 , Integrated Non-CO Observing System, 2018b), AGAGE, NOAA (Obspack Globalview CH: 10.25925/20221001 , Schuldt et al., 2017), CSIRO and/or WMO GAW. We thank all station PIs and their organizations for providing these valuable datasets. We acknowledge the work of other members of the EDGAR group (Edwin Schaaf, Jos Olivier) and the outstanding scientific contribution to the VERIFY project of Peter Bergamaschi. Timo Vesala thanks ICOS-Finland, University of Helsinki. The TM5-CAMS inversions are available from https://atmosphere.copernicus.eu (last access: June 2022); Arjo Segers acknowledges support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission (grant no. CAMS2_55). This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810). Ronny Lauerwald received support from the CLand Convergence Institute. Prabir Patra received support from the Environment Research and Technology Development Fund (grant no. JPMEERF20182002) of the Environmental Restoration and Conservation Agency of Japan. Pierre Regnier received financial support from the H2020 project ESM2025 – Earth System Models for the Future (grant no. 101003536). David Basviken received support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (METLAKE, grant no. 725546). Greet Janssens-Maenhout received support from the European Union's Horizon 2020 research and innovation program (CoCO, grant no. 958927). Tuula Aalto received support from the Finnish Academy (grants nos. 351311 and 345531). Sönke Zhaele received support from the ERC consolidator grant QUINCY (grant no. 647204).Peer reviewedPublisher PD

Overview: Integrative and Comprehensive Understanding on Polar Environments (iCUPE) – concept and initial results

The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. We set up the ERA-PLANET Strand 4 project “iCUPE – integrative and Comprehensive Understanding on Polar Environments” to provide novel insights and observational data on global grand challenges with an Arctic focus. We utilize an integrated approach combining in situ observations, satellite remote sensing Earth observations (EOs), and multi-scale modeling to synthesize data from comprehensive long-term measurements, intensive campaigns, and satellites to deliver data products, metrics, and indicators to stakeholders concerning the environmental status, availability, and extraction of natural resources in the polar areas. The iCUPE work consists of thematic state-of-the-art research and the provision of novel data in atmospheric pollution, local sources and transboundary transport, the characterization of arctic surfaces and their changes, an assessment of the concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, the quantification of emissions from natural resource extraction, and the validation and optimization of satellite Earth observation (EO) data streams. In this paper we introduce the iCUPE project and summarize initial results arising out of the integration of comprehensive in situ observations, satellite remote sensing, and multi-scale modeling in the Arctic context

The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom:1990-2020

Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural (including formally managed ecosystems) sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed and unmanaged ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven categorical model results, and inverse modeling estimates, extending the previous period 1990-2018 to the year 2020 to the extent possible. BU and TD products are compared with the European national greenhouse gas inventory (NGHGI) reported by parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation 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), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing the NGHGI with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from land use, land use change, and forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 926g±g13gTggCgyr-1, while eight other BU sources report a mean value of 948 [937,961]gTggCgyr-1 (25th, 75th percentiles). The sole top-down inversion of fossil emissions currently available accounts for 875gTggC in this same year, a value outside the uncertainty of both the NGHGI and bottom-up ensemble estimates and for which uncertainty estimates are not currently available. For the net CO2 land fluxes, during the most recent 5-year period including the NGHGI estimates, the NGHGI accounted for -91g±g32gTggCgyr-1, while six other BU approaches reported a mean sink of -62 [-117,-49]gTggCgyr-1, and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 [-152,-5]gTggCgyr-1. The 5-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73gTggCgyr-1 has a slightly smaller spread (0th-100th percentiles of [-135,+45]gTggCgyr-1), and it was calculated after removing net land-atmosphere CO2 fluxes caused by lateral transport of carbon (crop trade, wood trade, river transport, and net uptake from inland water bodies), resulting in increased agreement with the NGHGI and bottom-up approaches. Results at the category level (Forest Land, Cropland, Grassland) generally show good agreement between the NGHGI and category-specific models, but results for DGVMs are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find that current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while land fluxes are relatively stable: positive or negative trends larger (smaller) than 0.07 (-0.61)gTggCgyr-2 can be ruled out for the NGHGI. In addition, a gap on the order of 1000gTggCgyr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial, in addition to known uncertainties in top-down estimates of the land fluxes. The data used to plot the figures are available at 10.5281/zenodo.8148461 (McGrath et al., 2023).</p

Overview : Integrative and Comprehensive Understanding on Polar Environments (iCUPE) - concept and initial results

The role of polar regions is increasing in terms of megatrends such as globalization, new transport routes, demography, and the use of natural resources with consequent effects on regional and transported pollutant concentrations. We set up the ERA-PLANET Strand 4 project "iCUPE - integrative and Comprehensive Understanding on Polar Environments" to provide novel insights and observational data on global grand challenges with an Arctic focus. We utilize an integrated approach combining in situ observations, satellite remote sensing Earth observations (EOs), and multi-scale modeling to synthesize data from comprehensive long-term measurements, intensive campaigns, and satellites to deliver data products, metrics, and indicators to stakeholders concerning the environmental status, availability, and extraction of natural resources in the polar areas. The iCUPE work consists of thematic state-of-the-art research and the provision of novel data in atmospheric pollution, local sources and transboundary transport, the characterization of arctic surfaces and their changes, an assessment of the concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, the quantification of emissions from natural resource extraction, and the validation and optimization of satellite Earth observation (EO) data streams. In this paper we introduce the iCUPE project and summarize initial results arising out of the integration of comprehensive in situ observations, satellite remote sensing, and multi-scale modeling in the Arctic context.Peer reviewe