Effects of extreme meteorological conditions in 2018 on European methane emissions estimated using atmospheric inversions

The effect of the 2018 extreme meteorological conditions in Europe on methane (CH 4 ) emissions is examined using estimates from four atmospheric inversions calculated for the period 2005–2018. For most of Europe, we find no anomaly in 2018 compared to the 2005–2018 mean. However, we find a positive anomaly for the Netherlands in April, which coincided with positive temperature and soil moisture anomalies suggesting an increase in biogenic sources. We also find a negative anomaly for the Netherlands for September–October, which coincided with a negative anomaly in soil moisture, suggesting a decrease in soil sources. In addition, we find a positive anomaly for Serbia in spring, summer and autumn, which coincided with increases in temperature and soil moisture, again suggestive of changes in biogenic sources, and the annual emission for 2018 was 33 ± 38% higher than the 2005–2017 mean. These results indicate that CH 4 emissions from areas where the natural source is thought to be relatively small can still vary due to meteorological conditions. At the European scale though, the degree of variability over 2005–2018 was small, and there was negligible impact on the annual CH 4 emissions in 2018 despite the extreme meteorological conditions. This article is part of a discussion meeting issue ‘Rising methane: is warming feeding warming? (part 2)’.

How do Cl concentrations matter for simulating CH₄, δ¹³C(CH₄) and estimating CH₄ budget through atmospheric inversions?

Atmospheric methane (CH4) concentrations have been rising since 2007, resulting from an imbalance between CH4 sources and sinks. The CH4 budget is generally estimated through top-down approaches using CH4 observations as constraints. The atmospheric isotopic CH4 signal, δ13C(CH4), can also provide additional constraints and helps to discriminate between emission categories. The oxidation by chlorine (Cl) likely contributes less than 5 % to the total oxidation of atmospheric CH4. However, the Cl sink is highly fractionating, and thus strongly influences δ13C(CH4). As inversion studies do not prescribe the same Cl fields to constrain CH4 budget, it can lead to discrepancies between estimates. To quantify the influence of the Cl concentrations on CH4, δ13C(CH4) and CH4 budget estimates, we perform multiple sensitivity simulations using three Cl fields with concentrations that are realistic with regard to recent literature and one Cl field with concentrations that are very likely to be overestimated. We also test removing the tropospheric and the entire Cl sink in other sensitivity simulations. We find that the realistic Cl fields tested here are responsible for between 0.3 % and 1.8 % of the total chemical CH4 sink in the troposphere and between 1.0 % and 1.2 % in the stratosphere. Prescribing these different Cl amounts in surface-based inversions can lead to differences in global CH4 source adjustments of up to 12.3 TgCH4.yr−1. We also find that the globally-averaged isotopic signature of the CH4 sources inferred by a surface-based inversion assimilating δ13C(CH4) observations would decrease by 0.53 ‰ for each additional percent of contribution from the tropospheric Cl sink to the total sink. Finally, our study shows that CH4 seasonal cycle amplitude is modified by less than 1–2 % but δ13(CH4) seasonal cycle amplitude can be modified by up to 10–20 %, depending on the latitude.

Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions

In support of the Global Stocktake of the Paris Agreement on Climate change, this study presents a comprehensive framework to process the results of atmospheric inversions in order to make them suitable for evaluating UNFCCC national inventories of land-use carbon dioxide (CO2) emissions and removals, corresponding to the Land Use, Land Use Change and Forestry and waste sectors. We also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, and anthropogenic nitrous oxide (N2O) emissions from inversions. To compare inversions with national reports, we compiled a new global harmonized database of national emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by Non-Annex I countries, given by National Communications and Biennial Update Reports. The method to reconcile inversions with inventories is applied to selected large countries covering 78 % of the global land carbon uptake for CO2, as well as emissions and removals in the land use, land use change and forestry sector, and top-emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products, and the role of carbon uptake in unmanaged lands, both not accounted for by the rules of inventories. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in-situ network and those using satellite CH4 retrievals, and a tendency for inversions to diagnose higher CH4 emissions estimates than reported by inventories. In particular, oil and gas extracting countries in Central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to either a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, or to the existence of natural sources intertwined with managed lands, or to an under-estimation of N2O emission factors for direct agricultural soil emissions. The advantage of inversions is that they provide insights on seasonal and interannual greenhouse gas fluxes anomalies, e.g. during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges.

Time-series of GhG emissions estimation for the learning of inverse modeling

<p>Inverse modeling is a commonly used method to infer greenhouse gases (GhGs) sources and sinks based on their observed concentrations in the atmosphere. It is a Bayesian framework and requires a priori fluxes of all the evaluated sources and sinks, atmospheric observations, an atmospheric transport model that relates the observations to the a priori fluxes and the uncertainties of both fluxes and observations. Various techniques exist to solve the inversion.</p><p>Atmospheric inverse modeling is and will become even more important in the future quantification of GhGs to monitor the compliance of the Nationally Determined Contributions (NDCs) under the Paris Agreement. Therefore, the scientific and educational communities are becoming more interested in using atmospheric inversions and this has risen a necessity of creating tools that facilitate understanding as well as training in these techniques.</p><p>Quantifying anthropogenic GhG emissions, such as CO<sub>2</sub> from fossil fuel burning or CH<sub>4 </sub>from human activities, from atmospheric concentration observations is difficult since the carbon from all sources, both natural and anthropogenic, is mixed in the atmosphere, making it necessary to use other signals or tracers to separate anthropogenic emissions from natural sources. For fossil fuel CO<sub>2</sub> emissions radiocarbon (<sup>14</sup>CO<sub>2</sub>) is an excellent emission tracer because, due to its radioactive decay (~ 5000 years), it cannot be found in fossil fuels, which have been deposited millions of years ago as organic material. We have developed a Jupyter Notebook based on Python for the quantification of multi-tracer GhGs fossil fuel emissions and its isotopes. The notebook solves for the emissions by applying atmospheric inversions within a practical two-box model. The inverse modeling notebook is based on the analytical maximum a posteriori (MAP) solution of the Bayes’ theorem and allows to assess the error in the state vector and its uncertainty.</p><p>This basic but powerful notebook is meant to be an educational and training tool for university students and new researchers in the field as well as for researchers interested in the estimation of long-term (>centennial) time-series of GhG emissions since it is built as a modular algorithm to be easily modified, coupled or expanded to other approaches or models depending on the application. The notebook was initially developed for the inverse modeling of CO<sub>2</sub> and <sup>14</sup>CO<sub>2</sub> simultaneously and it is being expanded for additional GHG such as CH<sub>4</sub> and <sup>13</sup>CH<sub>4</sub>.</p>

Local-scale atmospheric inversions to monitor CH4 emissions from industrial sites using mobile and/or fixed-point measurements

<p>The efficient and precise monitoring (detection, localization, and quantification) of fugitive methane (CH<sub>4</sub>) emissions is essential in preventing and mitigating greenhouse gas (GHG) emissions from oil and gas industrial facilities and landfills. Various strategies of mole fraction sampling within or in the vicinity of the sites and of atmospheric inversions have been developed for such a monitoring. Many studies have ensured the constant improvement of instrumentation, of measurement strategies and atmospheric inversion techniques.</p><p>In this context, we participated in two controlled-release experiments at the TOTAL Anomaly Detection Initiatives (TADI) test site (Lacq, France) in October 2018 and 2019, dedicated to evaluate the ability of different local-scale atmospheric measurement and inverse modeling systems to localize and quantify point sources. We also conducted a series of 18 campaigns to regularly quantify methane emissions from the active “Butte-Bellot” landfill (about 35 km south-east of Paris) since 2018. We developed and applied different inversion approaches to process mobile or fixed-point measurements, which, in both cases, rely on a Gaussian dispersion model to simulate the atmospheric plume from the potential source location or mole fraction sensitivity at the measurement time and location to emissions at the potential source locations.</p><p>The series of CH<sub>4</sub> and carbon dioxide (CO<sub>2</sub>) controlled releases in TADI covered a wide range of release rates (~0.1 to 200 gCH<sub>4</sub>/s and 0.2 to 200 gCO<sub>2</sub>/s) and durations from 4 to 8 minutes (brief) to 25 to 75 minutes (longer). During the corresponding campaigns, we conducted both near-surface mobile and fixed-point (~2-4 m height) in situ atmospheric measurements based on Picarro CRDS, LGR (MGGA and UGGA), and LI-COR (LI-7810) instruments. Both inversions based on mobile measurements and those based on the fixed-point measurements provide estimates with a 20-30% average error for the CH<sub>4</sub> and CO<sub>2</sub> release rates, whatever the duration of the releases. The use of fixed-point measurements during long releases allow for a more precise localization of sources with an average location error of ~8m.</p><p>The analysis of the CH<sub>4 </sub>mobile measurements at the “Butte-Bellot” landfill reveals the difficulties in exploiting measurements close to such a site with diffuse emissions whose spatial distribution is difficult to characterize, heterogeneous and highly variable in time. The series of estimates of the total CH<sub>4</sub> emissions from the site based on remote mobile plume cross-sections, based on atmospheric inversions, are discussed.</p><p>This presentation will highlight positive perspectives opened by the proposed inversion approaches and by our results and discuss options for further improvements when processing both types of measurements.</p>

Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)

Regional land carbon budgets provide insights on the spatial distribution of the land uptake of atmospheric carbon dioxide, and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions, due to different definitions and component fluxes reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers, that connect CO2 uptake in one area with its release in another also requires better definition and protocols to reach harmonized regional budgets that can be summed up to the globe and compared with the atmospheric CO2 growth rate and inversion results. In this study, for the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims as an update of regional carbon budgets over the last two decades based on observations, for 10 regions covering the globe, with a better harmonization that the precursor project, we provide recommendations for using atmospheric inversions results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes and land use fluxes.

SMOS-IC L-VOD reveals that tropical forests did not recover from the strong 2015–2016 El Niño event

<p>Severe drought and extreme heat associated with the 2015–2016 El Niño event have led to large carbon emissions from the tropical vegetation to the atmosphere. With the return to normal climatic conditions in 2017, tropical forest aboveground carbon (AGC) stocks are expected to partly recover due to increased productivity, but the intensity and spatial distribution of this recovery are unknown. Simulations from land-surface models used in the global carbon budget (GCB) suggest a strong reinvigoration of the tropical land sink after the 2015–2016 El Niño. However, models and atmospheric inversions display large divergences in tropical CO<sub>2</sub> fluxes during the 2017 recovery event. For instance, models predict a total net land sink recovery (2017 sink minus the 2015–2016 average sink) ranging from 0.3 to 2.6 Pg C, and the land sink recovery estimated from five atmospheric inversions ranges from −0.08 to +1.92 Pg C. The results of different inversions show a large spread in the tropics due to the scarcity of stations and uncertainties in atmospheric transport simulations.</p><p>We used low-frequency microwave satellite data (L-VOD) to feature precise monitoring of AGC changes and show that the AGC recovery of tropical ecosystems was slow and that by the end of 2017, AGC had not reached predrought levels of 2014<sup>1</sup>. From 2014 to 2017, tropical AGC stocks decreased by 1.3 Pg C due to persistent AGC losses in Africa (-0.9 Pg C) and America (-0.5 Pg C). Pantropically, drylands recovered their carbon stocks to pre–El Niño levels, but African and American humid forests did not, suggesting carryover effects from enhanced forest mortality.</p><p> </p><p><strong>Reference</strong></p><ol><li>J.-P. Wigneron, L. Fan, P. Ciais, A. Bastos, M. Brandt, J. Chave, S. Saatchi, A. Baccini, R. Fensholt, Tropical forests did not recover from the strong 2015–2016 El Niño event. Science Advances. 6, eaay4603 (2020).</li> </ol>