Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS

Abstract

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).

Atmospheric Methane: Comparison Between Methane's Record in 2006–2022 and During Glacial Terminations

“…The bulk source of global methane emissions has 𝐴𝐴 𝐴𝐴 13 CCH4 ≅ −53‰ for all sources (Dlugokencky et al, 2011;Nisbet et al, 2019;Sherwood et al, 2017). There is urgent need for better information on isotopic signatures of sources (Thanwerdas et al, 2022). Oh et al (2022) CCH4 (lower panel) for five long term monitoring sites in each hemisphere.…”

Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020

“…The role of the Cl sink in the troposphere is highly uncertain due to a lack of consensus on the strength and distribution of the tropospheric Cl sink and trend 25,[47][48][49] . To check the sensitivity of Cl sink on CH4 and δ 13 C-CH4 trends, we did simulations using the cyclostationary Cl field from Wang et al 49 (Cl_wang) in addition to control Cl field (Cl_ctrl; Takigawa et al 50 ).…”

“…Previous studies have adopted either one of these values (L-KIEOH vs H-KIEOH) based on their preference [14][15][16][17][18]24,25,32,46,48,53 . Since we cannot determine the relative merits of the reported OH fractionation, we evaluate both (L-KIEOH , H-KIEOH) values to understand the impact of the choice of KIEOH values on δ 13 C-CH4 simulations.…”

“…Conversely, emissions from thermogenic sectors (δ 13 C-CH4 in the range of about -25 to -55‰) such as fossil fuel exploitation, geological sources, and pyrogenic emissions from biomass burning sources (δ 13 C-CH4 in the range of about -13 to -26‰), as well as all methane sinks, induce an increase of atmospheric δ 13 C-CH4, except light fossil fuel sources 21,22 . By modeling CH4 isotopologues, it is possible to derive sector-specific emission estimates by utilizing source-specific isotope composition signatures and the kinetic isotope effect (KIE) of removal processes from the atmosphere at a global scale [14][15][16]23 and regional scale 17,18,24,25 . However, both the isotopic source signatures and KIEs have large uncertainties that can affect the estimation of the mean state of CH4 sectoral emissions.…”

Bottom‐Up Evaluation of the Methane Budget in Asia and Its Subregions

“…These data have been particularly effective in improving continental‐scale budget estimates, especially when used in combination with numerical atmospheric transport and chemistry models. A top‐down approach in which the surface budget is estimated via inverse modeling of atmospheric data has been used to evaluate spatial and temporal patterns of surface fluxes (e.g., Basu et al., 2022; Chandra et al., 2021; Lan et al., 2021; Patra et al., 2016; Thanwerdas et al., 2022; Thompson et al., 2015; Worden et al., 2022; Zhang et al., 2022). A top‐down approach is particularly effective for explaining the reasons underlying observed atmospheric CH 4 concentrations, but its limitations include a coarse spatial resolution (hundreds of kilometers), short temporal coverage due to limited availability of observational data, and imperfect separation of sources (Ghosh et al., 2015).…”