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We present a new method for partitioning observed CO2 enhancements (CO2xs) into fossil and biospheric fractions (Cff and Cbio) based on measurements of CO and δ13CO2, complemented by flask-based Δ14CO2 measurements. This method additionally partitions the fossil fraction into natural gas and petroleum fractions (when coal combustion is insignificant). Although here we apply the method only to discrete flask air measurements, the advantage of this method (CO and δ13CO2-based method) is that CO2xs partitioning can be applied at high frequency when continuous measurements of CO and δ13CO2 are available. High frequency partitioning of CO2xs into Cff and Cbio has already been demonstrated using continuous measurements of CO (CO-based method) and Δ14CO2 measurements from flask air samples. Relative to calculating CO2ff directly from Δ14CO2, we find that the uncertainty in CO2ff estimated from the CO and δ13CO2-based method averages 3.2 ppm which is significantly less than the CO-based method which has an average uncertainty of 4.8 ppm. Using measurements of CO, δ13CO2 and Δ14CO2 from flask air samples at three sites in the greater Los Angeles region, we find large contributions of biogenic sources that vary by season. On a monthly average, the biogenic signal accounts for -14 to +25 % of CO2xs with larger and positive contributions in winter and smaller and negative contributions in summer due to net respiration and net photosynthesis, respectively. Partitioning CO2ff into petroleum and natural gas combustion fractions reveals that the largest contribution of natural gas combustion generally occurs in summer, which is likely related to increased electricity generation in LA power plants for air-conditioning.

Since 1999, Environment and Climate Change Canada (ECCC) has been coordinating a multi-laboratory comparison of measurements of long-lived greenhouse gases in whole air samples collected at the Global Atmosphere Watch (GAW) Alert Observatory located in the Canadian high Arctic (82°28' N, 62°30' W). In this paper, we evaluate the measurement agreement of atmospheric CO2, CH4, N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) between leading laboratories from 7 independent international institutions. The measure of success is linked to target goals for network compatibility outlined by the World Meteorological Organization’s (WMO) GAW greenhouse gas measurement community. Overall, based on ~8000 discrete flask samples, we find that the co-located atmospheric CO2 and CH4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, ECCC, and NOAA are generally consistent with the WMO compatibility goals of ±0.1 ppm CO2 and ±2 ppb CH4 over the 17-year period (1999 – 2016), although there are periods where differences exceed target levels and persist as systematic bias for months or years. Consistency with the WMO goals for N2O, SF6, and stable isotopes of CO2 (δ13C, δ18O) has not been demonstrated. Additional analysis of co-located comparison measurements between CSIRO, SIO, and NOAA at other geographical sites suggests that the findings at Alert for CO2, CH4, N2O and δ13C-CO2 could be extended across the CSIRO, SIO, and NOAA observing networks. Two approaches are carried out to determine the level of agreement as a collective for the 7 individual laboratories (1) pooling the differences of individual laboratories over the entire sampling records from a designated reference laboratory and determining the 95th percentile range of these data points and (2) averaging the 2 standard deviations (2-sigma) of the means for all flask samples taken in each individual sampling episode over the entire sampling record. For CO2, from 5691 samples, we derive a measurement agreement level of -0.51 to +0.53 ppm using the 95th percentile range of the differences from NOAA measurements. Similarly, we derive a corresponding value of ± 0.37 ppm using the mean of 2-sigma values from 923 individual weekly sampling episodes. For CO2 isotopes using INSTAAR measurements as a reference, we derive measurement agreement values of -0.09 to +0.07 and ± 0.06 ‰ for δ13C and -0.50 to +0.58 and ± 0.31 ‰ for δ18O, for the 95th percentile ranges and the mean of the 2-sigma values, respectively. For other gases, the corresponding values for both approaches are 4.86 to +6.16 and ± 3.62 ppb for CH4, -0.75 to +1.20 and ± 0.64 ppb for N2O, and -0.14 to +0.09 and ± 0.09 ppt for SF6. These upper and lower limits represent our best estimate of the measurement agreement at the 95 % confidence level for these individual laboratories, providing more confidence for using these datasets in various scientific applications (e.g., long-term trend analysis).

Top-down greenhouse gas measurements can be used to independently assess the accuracy of bottom-up emission estimates. We report atmospheric methane (CH4) mole fractions and δ13CH4 measurements from Imperial College London from early 2018 onwards using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH4 mole fractions and δ13CH4 produced using the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and a global inventory, the Emissions Database for Global Atmospheric Research (EDGAR), with model spatial resolutions of ∼ 2, ∼ 10, and ∼ 25 km. Simulation–measurement comparisons are used to evaluate London emissions and the source apportionment in the global (EDGAR) and UK national (NAEI) emission inventories. Observed mole fractions were underestimated by 30 %–35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated δ13CH4 values for either NAEI or EDGAR, however, suggesting the inventories' sectoral attributions are incorrect. On average, natural gas sources accounted for 20 %–28 % of the above background CH4 in the NAEI simulations and only 6 %–9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than −45 ‰, suggesting the primary CH4 sources in London are actually natural gas leaks. The simulation–observation comparison of CH4 mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH4 yr−1) but close to, or slightly lower than, the EDGAR estimate (0.10 Tg CH4 yr−1). However, the simulation–observation comparison of δ13CH4 and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR.

Funding Information: Funding: We would like to thank the Magnus Ehrnrooth Foundation, the Vilho, Yrjö and Kalle Väisälä Foundation, Academy of Finland (307331 UPFORMET), EU-H2020 VERIFY, and ESA-MethEO, for financial support. The VERIFY project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 776810. Maarten Krol is supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 742798. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This study investigates the contribution of different CH4 sources to the seasonal cycle of δ13C during 2000–2012 by using the TM5 atmospheric transport model, including spatially varying information on isotopic signatures. The TM5 model is able to produce the background seasonality of δ13C, but the discrepancies compared to the observations arise from incomplete representation of the emissions and their source-specific signatures. Seasonal cycles of δ13C are found to be an inverse of CH4 cycles in general, but the anti-correlations between CH4 and δ13C are imperfect and experience a large variation (ρ = −0.35 to −0.91) north of 30° S. We found that wetland emissions are an important driver in the δ13C seasonal cycle in the Northern Hemisphere and Tropics, and in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of δ13C in July–October. The comparisons to the observations from 18 stations globally showed that the seasonal cycle of EFMM emissions in the EDGAR v5.0 inventory is more realistic than in v4.3.2. At northern stations (north of 55° N), modeled δ13C amplitudes are generally smaller by 12–68%, mainly because the model could not reproduce the strong depletion in autumn. This indicates that the CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. In addition, results from stations in northern latitudes (19–40° N) indicate that the proportion of biogenic to fossil-based emissions may need to be revised, such that a larger portion of fossil-based emissions is needed during summer.

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).

This study investigates the contribution of different CH4 sources to the seasonal cycle of 𝛿13C during years 2000–2012 using the TM5 atmospheric transport model. The seasonal cycles of anthropogenic emissions from two versions of the EDGAR inventories, v4.3.2 and v5.0 are examined. Those includes emissions from Enteric Fermentation and Manure Management (EFMM), rice cultivation and residential sources. Those from wetlands obtained from LPX-Bern v1.4 are also examined in addition to other sources such as fires and ocean sources. We use spatially varying isotopic source signatures for EFMM, coal, oil and gas, wetlands, fires and geological emission and for other sources a global uniform value. We analysed the results as zonal means for 30° latitudinal bands. Seasonal cycles of 𝛿13C are found to be an inverse of CH4 cycles in general, with a peak-to-peak amplitude of 0.07–0.26 ‰. However, due to emissions, the phase ellipses do not form straight lines, and the anti-correlations between CH4 and 𝛿13C are weaker (−0.35 to −0.91) in north of 30° S. We found that wetland emissions are the dominant driver in the 𝛿13C seasonal cycle in the Northern Hemisphere and Tropics, such that the timing of 𝛿13C seasonal minimum is shifted by ∼90 days in 60° N–90° N from the end of the year to the beginning of the year when seasonality of wetland emissions is removed. The results also showed that in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of 𝛿13C in July–October. In addition, we also compared the results against observations from the South Pole, Antarctica, Alert, Nunavut, Canada and Niwot Ridge, Colorado, USA. In light of this research, comparison to the observation showed that the seasonal cycle of EFMM emissions in EDGAR v5.0 inventory is more realistic than in v4.3.2. In addition, the comparison at Alert showed that modelled 𝛿13C amplitude was approximately half of the observations, mainly because the model could not reproduce the strong depletion in autumn. This indicates that CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. Results from Niwot Ridge indicate that in addition to biogenic emissions, the proportion of biogenic to fossil based emissions may need to be revised.

Assessment of bottom-up greenhouse gas emissions estimates through independent methods is needed to demonstrate whether reported values are accurate or if bottom-up methodologies need to be refined. We report atmospheric methane (CH4) mole fractions and δ13CH4 measurements from Imperial College London since early 2018 using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH4 mole fractions and δ13CH4 produced using the NAME dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and the global inventory, EDGAR, with model spatial resolutions of ~2 km, ~10 km, and ~25 km. Observed mole fractions were underestimated by 30–35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated δ13CH4 values for either NAEI or EDGAR, however, suggesting the inventories’ sectoral attributions are incorrect. On average, natural gas sources accounted for 20–28 % of the above background CH4 in the NAEI simulations, and only 6–9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than −45 ‰, suggesting the primary CH4 sources in London are actually natural gas leaks. The simulation-observation comparison of CH4 mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH4 yr−1) but close to, or slightly lower than the EDGAR estimate (0.10 Tg CH4 yr−1). However, the simulation-observation comparison of δ13CH4 and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR.

Atmospheric CH4 mixing ratios resumed their increase in 2007 after a plateau during the period 1999–2006, suggesting varying sources and sinks as main drivers. 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 attribute these changes to specific source variations. CH4 source isotopic signatures 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 emissions and associated 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 3-D Chemistry-Transport Model LMDz-SACS. We find that assimilating δ13C(CH4) observations and allowing the system to adjust source isotopic signatures provide relatively large differences in global flux estimates for wetlands (5 Tg yr−1), microbial (6 Tg yr−1), fossil fuels (8 Tg yr−1) and biofuels-biomass burning (4 Tg 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 source signatures are perfectly known increase these differences between the system with CH4 and the enhanced one with δ13C(CH4) by a factor 3 or 4, strengthening the importance of having as accurate as possible signatures. Initial conditions, uncertainties on δ13C(CH4) observations or the number of optimized categories have a much smaller impact (less than 2 Tg yr−1).

Additional text details and figures for Lan et al.

Atmospheric flask samples are either collected at atmospheric pressure by opening a valve of a pre-evacuated flask or pressurized with the help of a pump to a few bar above ambient pressure. Under humid conditions, there is a risk that water vapor in the sample leads to condensation on the walls of the flask, notably at higher than ambient sampling pressures. Liquid water in sample flasks is known to affect the CO2 mixing ratios and also alters the isotopic composition of oxygen (17O and 18O) in CO2 via isotopic equilibration. Hence, for accurate determination of CO2 mole fractions and its stable isotopic composition, it is vital to dry the air samples to a sufficiently low dew point before they are pressurized in flasks to avoid condensation. Moreover, the drying system itself should not influence the mixing ratio and the isotopic composition of CO2 or that of the other constituents under study. For the Airborne Stable Isotopes of Carbon from the Amazon (ASICA) project focusing on accurate measurements of CO2 and its singly substituted stable isotopologues over the Amazon, an air-drying system capable of removing water vapor from air sampled at a dew point lower than -2 °C, flow rates up to 12 L min-1 and without the need for electrical power was needed. Since to date no commercial air-drying device that meets these requirements has been available, we designed and built our own consumable-free, power-free and portable drying system based on multitube Nafion™ gas sample driers (Perma Pure, Lakewood, USA). The required dry purge air is provided by feeding the exhaust flow of the flask sampling system through a dry molecular sieve (type 3A) cartridge. In this study we describe the systematic evaluation of our Nafion™-based air sample dryer with emphasis on its performance concerning the measurements of atmospheric CO2 mole fractions and the three singly substituted isotopologues of CO2 (16O13C16O, 16O12C17O and 16O12C18O), as well as the trace gas species CH4, CO, N2O and SF6. Experimental results simulating extreme tropical conditions (saturated air at 33 °C) indicated that the response of the air dryer is almost instantaneous and that approximately 85 L of air, containing up to 4 % water vapor, can be processed staying below a -2 °C dew point temperature (at 275 kPa). We estimated that at least eight flasks can be sampled (at an overpressure of 275 kPa) with a water vapor content below -2 °C dew point temperature during a typical flight sampling up to 5 km altitude over the Amazon, whereas the remaining samples would stay well below 5 °C dew point temperature (at 275 kPa). The performance of the air dryer on measurements of CO2, CH4, CO, N2O, and SF6 and the CO2 isotopologues 16O13C16O and 16O12C18O was tested in the laboratory simulating real sampling conditions by compressing humidified air from a calibrated cylinder, after being dried by the air dryer, into sample flasks. We found that the mole fraction and the isotopic composition difference between the different test conditions (including the dryer) and the base condition (dry air, without dryer) remained well within or very close to, in the case of N2O, the World Meteorological Organization recommended compatibility goals for independent measurement programs, proving that the test condition induced no significant bias on the sample measurements.