Abstract

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).

Highlights

  • 20 Methane (CH4) is a powerful greenhouse gas and is responsible for 23 % (Etminan et al, 2016) of the radiative forcing induced by the well-mixed greenhouse gases (CO2, CH4, N2O)

  • 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-Simplified Atmospheric Chemistry System (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 15 δ13C(CH4) observations

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Summary

Introduction

20 Methane (CH4) is a powerful greenhouse gas and is responsible for 23 % (Etminan et al, 2016) of the radiative forcing induced by the well-mixed greenhouse gases (CO2, CH4, N2O). Atmospheric CH4 mixing ratios have increased quasi-continuously since the pre-industrial era and by about 9 ppb/yr from 1984 to 1998 (www.esrl.noaa.gov/gmd/ccgg/trends_ch4/). Plateau between 1999 and 2006 that still generates attention and controversy (e.g., Fujita et al, 2020; Thompson et al, 2018; McNorton et al, 2018; Turner et al, 2017; Schaefer et al, 2016; Schwietzke et al, 2016; Rice et al, 2016), the mixing ratios resumed their increase at a large rate, exceeding 10 ppb/yr in 2014 and 2015. Trends in atmospheric CH4 are caused by a small imbalance between large sources and sinks. Assessing their spatio-temporal characteristics is challenging 5 considering the variety of methane emissions. Identifying and quantifying the processes contributing to these changes is mandatory to formulate relevant CH4 mitigation policies that would contribute to meet the target of the 2015 UN Paris Agreement on Climate Change and to limit climate warming to 2 °C

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