Abstract. Characterizing methane sources in the Arctic remains challenging due to the remoteness, heterogeneity and variety of such emissions. In situ campaigns provide valuable datasets to reduce these uncertainties. Here we analyse data from the summer 2014 SWERUS-C3 campaign in the eastern Arctic Ocean, off the shore of Siberia and Alaska. Total concentrations of methane, as well as relative concentrations of 12CH4 and 13CH4, were measured continuously during this campaign for 35 d in July and August. Using a chemistry-transport model, we link observed concentrations and isotopic ratios to regional emissions and hemispheric transport structures. A simple inversion system helped constrain source signatures from wetlands in Siberia and Alaska, and oceanic sources, as well as the isotopic composition of lower-stratosphere air masses. The variation in the signature of lower-stratosphere air masses, due to strongly fractionating chemical reactions in the stratosphere, was suggested to explain a large share of the observed variability in isotopic ratios. These results point towards necessary efforts to better simulate large-scale transport and chemistry patterns to make relevant use of isotopic data in remote areas. It is also found that constant and homogeneous source signatures for each type of emission in a given region (mostly wetlands and oil and gas industry in our case at high latitudes) are not compatible with the strong synoptic isotopic signal observed in the Arctic. A regional gradient in source signatures is highlighted between Siberian and Alaskan wetlands, the latter having lighter signatures (more depleted in 13C). Finally, our results suggest that marine emissions of methane from Arctic continental-shelf sources are dominated by thermogenic-origin methane, with a secondary biogenic source as well.

The scrutiny over the carbon footprint of research and higher education has in- creased rapidly in the last few years. This has resulted in a series of publications providing various estimates of the carbon footprint of one or several research activities, principally at the scale of a university or a research center or, more recently, a field of research. The variety of tools or methodologies on which these estimates rely unfortu- nately prevents any aggregation or direct comparison. This is because carbon footprint assessments are very sensitive to key parameters (e.g., emission factors) or hypotheses (e.g., scopes). Hence, it is impossible to address fundamental questions such as: is the carbon footprint of research structurally different between disciplines? Are plane trips a major source of carbon emissions in academic research? Massive collection and cura- tion of carbon footprint data, across a large array of research situations and disciplines, is hence an important, timely and necessary challenge to answer these questions. This paper presents a framework to collect and analyse large amounts of homoge- neous research carbon emission data in a network of research entities at the national scale. It relies on an open-source web application, GES 1point5, designed to estimate the carbon footprint of a department, research lab or team in any country of the world. Importantly, GES 1point5 is also designed to aggregate all input data and correspond- ing GHG emissions estimates into a comprehensive database. GES 1point5 therefore enables (i) the identification of robust local or national determinants of research carbon footprint and (ii) the estimation of the carbon footprint of the entire research sector at national scale. A preliminary analysis of the carbon footprint of more than one hun- dred laboratories in France is presented to illustrate the potential of the framework. It shows that the average emissions are 479 t CO2e for a research lab and 3.6 t CO2e for an average lab member (respectively 404 and 3.1 t CO2e without accounting for the indirect radiative effects of aviation), with the current scope of GES 1point5.

Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.

Abstract. Atmospheric CH4 mole fractions resumed their increase in 2007 after a plateau during the 1999–2006 period, indicating relative changes in the sources and sinks. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is, nevertheless, challenging to efficiently differentiate co-located emission categories and sinks by using CH4 observations alone. As a result, top-down approaches are limited when it comes to fully understanding CH4 burden changes and attributing these changes to specific source variations. δ13C(CH4)source isotopic signatures of CH4 sources differ between emission categories (biogenic, thermogenic, and pyrogenic) and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. This system is capable of optimizing both the emissions and the associated source signatures of multiple emission categories at the pixel scale. To our knowledge, this represents the first attempt to carry out variational inversion assimilating δ13C(CH4) with a 3-D chemistry transport model (CTM) and to independently optimize isotopic source signatures of multiple emission categories. We present the technical implementation of joint CH4 and δ13C(CH4) constraints in a variational system and analyze how sensitive the system is to the setup controlling the optimization using the LMDz-SACS 3-D CTM. We find that assimilating δ13C(CH4) observations and allowing the system to adjust isotopic source signatures provide relatively large differences in global flux estimates for wetlands (−5.7 Tg CH4 yr−1), agriculture and waste (−6.4 Tg CH4 yr−1), fossil fuels (+8.6 Tg CH4 yr−1) and biofuels–biomass burning (+3.2 Tg CH4 yr−1) categories compared to the results inferred without assimilating δ13C(CH4) observations. More importantly, when assimilating both CH4 and δ13C(CH4) observations, but assuming that the source signatures are perfectly known, these differences increase by a factor of 3–4, strengthening the importance of having as accurate signature estimates as possible. Initial conditions, uncertainties in δ13C(CH4) observations, or the number of optimized categories have a much smaller impact (less than 2 Tg CH4 yr−1).

<p><strong>Abstract.</strong> Methane (CH<sub>4</sub>) is the second strongest anthropogenic greenhouse gas after carbon dioxide (CO<sub>2</sub>) and is responsible for about 20 % of the warming induced by long-lived greenhouse gases since pre-industrial times. Oxidation by the hydroxyl radical (OH) is the dominant atmospheric sink for methane, contributing to approximately 90 % of the total methane loss. Chemical losses by reaction with atomic oxygen (O<sup>1</sup>D) and chlorine radicals (Cl) in the stratosphere are other sinks, contributing about 3 % to the total methane destruction. Moreover, the reaction with Cl is very fractionating, thus it has a much larger impact on <i>δ</i><sup>13</sup>C-CH<sub>4</sub> than the reaction with OH. In this paper, we assess the impact of atomic Cl on atmospheric methane mixing ratios, methane atmospheric loss and atmospheric <i>δ</i><sup>13</sup>C-CH<sub>4</sub>. The offline version of the Global Circulation Model (GCM) LMDz, coupled to a chemistry module including the major methane chemical reactions, is run to simulate CH<sub>4</sub> concentrations and <i>δ</i><sup>13</sup>C-CH<sub>4</sub> at the global scale. Atmospheric methane sink by Cl atoms in the stratosphere is found to be 7.32 ± 0.16 Tg/yr. Methane observations from vertical profiles obtained using AirCore samplers above 11 different locations across the globe and balloon measurements of <i>δ</i><sup>13</sup>C-CH<sub>4</sub> and methane are used to assess the impact of the Cl sink in the chemistry transport model. Above 10 km, the presence of Cl in the model is found to have only a small impact on the vertical profile of total methane but a major influence on <i>δ</i><sup>13</sup>C-CH<sub>4</sub> values, significantly improving the agreement between simulations and available observations. Stratospheric Cl is also found to have a substantial impact on surface <i>δ</i><sup>13</sup>C-CH<sub>4</sub> values, leading to a difference of +0.27 ‰ (less negative values) after a 19-year run. As a result, this study suggests that the Cl sink needs to be properly taken into account (magnitude and trends) in order to better understand trends in the atmospheric <i>δ</i><sup>13</sup>C-CH<sub>4</sub> signal when using atmospheric chemistry transport models for forward or inverse calculations.</p>

Abstract. Atmospheric methane (CH4) concentrations have been rising since 2007 due to an imbalance between CH4 sources and sinks. The CH4 budget is generally estimated through top-down approaches using chemistry transport models (CTMs) and CH4 observations as constraints. The atmospheric isotopic CH4 composition, δ13C(CH4), can also provide additional constraints and helps to discriminate between emission categories. Nevertheless, to be able to use the information contained in these observations, the models must correctly account for processes influencing δ13C(CH4). The oxidation by chlorine (Cl) likely contributes less than 5 % to the total oxidation of atmospheric CH4. However, the large kinetic isotope effect of the Cl sink produces a large fractionation of 13C, compared with 12C in atmospheric CH4, and thus may strongly influence δ13C(CH4). When integrating the Cl sink in their setup to constrain the CH4 budget, which is not yet standard, atmospheric inversions prescribe different Cl fields, therefore leading to discrepancies between flux estimates. To quantify the influence of the Cl concentrations on CH4, δ13C(CH4), and CH4 budget estimates, we perform sensitivity simulations using four different Cl fields. We also test removing the tropospheric and the entire Cl sink. We find that the Cl fields tested here are responsible for between 0.3 % and 8.5 % of the total chemical CH4 sink in the troposphere and between 1.0 % and 1.6 % in the stratosphere. Prescribing these different Cl amounts in atmospheric inversions can lead to differences of up to 53.8 Tg CH4 yr−1 in global CH4 emissions and of up to 4.7 ‰ in the globally averaged isotopic signature of the CH4 source δ13C(CH4)source), although these differences are much smaller if only recent Cl fields are used. More specifically, each increase by 1000 molec.cm-3 in the mean tropospheric Cl concentration would result in an adjustment by +11.7 Tg CH4 yr−1, for global CH4 emissions, and −1.0 ‰, for the globally averaged δ13C(CH4)source. Our study also shows that the CH4 seasonal cycle amplitude is modified by less than 1 %–2 %, but the δ13C(CH4) seasonal cycle amplitude can be significantly modified by up to 10 %–20 %, depending on the latitude. In an atmospheric inversion performed with isotopic constraints, this influence can result in significant differences in the posterior source mixture. For example, the contribution from wetland emissions to the total emissions can be modified by about 0.8 % to adjust the globally averaged δ13C(CH4)source, corresponding to a 15 Tg CH4 yr−1 change. This adjustment is small compared to the current wetland source uncertainty, albeit far from negligible. Finally, tested Cl concentrations have a large influence on the simulated δ13C(CH4) vertical profiles above 30 km and a very small impact on the simulated CH4 vertical profiles. Overall, our model captures the observed CH4 and δ13C(CH4) vertical profiles well, especially in the troposphere, and it is difficult to prefer one Cl field over another based uniquely on the available observations of the vertical profiles.

<p><strong>Abstract.</strong> Due to the large variety and heterogeneity of sources in remote areas hard to document, the Arctic regional methane budget remain very uncertain. In situ campaigns provide valuable data sets to reduce these uncertainties. Here we analyse data from the SWERUS-C3 campaign, on-board the icebreaker <i>Oden</i>, that took place during summer 2014 in the Arctic Ocean along the Northern Siberian and Alaskan shores. Total concentrations of methane, as well as isotopic ratios were measured continuously during this campaign for 35 days in July and August 2014. Using a chemistry-transport model, we link observed concentrations and isotopic ratios to regional emissions and hemispheric transport structures. A simple inversion system helped constraining source signatures from wetlands in Siberia and Alaska and oceanic sources, as well as the isotopic composition of lower stratosphere air masses. The variation in the signature of low stratosphere air masses, due to strongly fractionating chemical reactions in the stratosphere, was suggested to explain a large share of the observed variability in isotopic ratios. These points at required efforts to better simulate large scale transport and chemistry patterns to use isotopic data in remote areas. It is found that constant and homogeneous source signatures for each type of emission in the region (mostly wetlands and oil and gas industry) is not compatible with the strong synoptic isotopic signal observed in the Arctic. A regional gradient in source signatures is highlighted between Siberian and Alaskan wetlands, the later ones having a lighter signatures than the first ones. Arctic continental shelf sources are suggested to be a mixture of methane from a dominant thermogenic origin and a secondary biogenic one, consistent with previous in-situ isotopic analysis of seepage along the Siberian shores.</p>