Global Atmospheric <i>δ</i><sup>13</sup>CH<sub>4</sub> and CH<sub>4</sub> Trends for 2000–2020 from the Atmospheric Transport Model TM5 Using CH<sub>4</sub> from Carbon Tracker Europe–CH<sub>4</sub> Inversions
Vilma Mannisenaho,
Aki Tsuruta,
Leif Backman,
Sander Houweling,
Arjo Segers,
Maarten Krol,
Marielle Saunois,
Benjamin Poulter,
Zhen Zhang,
Xin Lan,
Edward J. Dlugokencky,
Sylvia Michel,
James W. C. White,
Tuula Aalto
Affiliations
Vilma Mannisenaho
Finnish Meteorological Institute, FI-00101 Helsinki, Finland
Aki Tsuruta
Finnish Meteorological Institute, FI-00101 Helsinki, Finland
Leif Backman
Finnish Meteorological Institute, FI-00101 Helsinki, Finland
Sander Houweling
SRON Netherlands Institute for Space Research, 2333 CA Leiden, The Netherlands
Arjo Segers
Department of Climate, Air & Sustainability, Netherlands Organisation for Applied Scientific Research (TNO), 3508 TA Utrecht, The Netherlands
Maarten Krol
Department of Environmental Sciences, Wageningen University & Research, Meteorology and Air Quality, 6700 AA Wageningen, The Netherlands
Marielle Saunois
Laboratoire des Sciences du Climat et de l’Environnement, MCF–Université de Versailles Saint-Quentin, CEA-Orme des Merisiers, 91191 Gif-sur-Yvette, France
Benjamin Poulter
NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD 20771, USA
Zhen Zhang
Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Ct Suite 4001, College Park, MD 20740, USA
Xin Lan
NOAA Global Monitoring Laboratory (GML), 325 Broadway, Boulder, CO 80305, USA
Edward J. Dlugokencky
NOAA Global Monitoring Laboratory (GML), 325 Broadway, Boulder, CO 80305, USA
Sylvia Michel
Institute of Arctic and Alpine Research (INSTAAR), University of Colorado, Campus Box 450, Boulder, CO 80309, USA
James W. C. White
Department of Earth Marine and Environmental Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
Tuula Aalto
Finnish Meteorological Institute, FI-00101 Helsinki, Finland
This study investigates atmospheric δ13CH4 trends, as produced by a global atmospheric transport model using CH4 inversions from CarbonTracker-Europe CH4 for 2000–2020, and compares them to observations. The CH4 inversions include the grouping of the emissions both by δ13CH4 isotopic signatures and process type to investigate the effect, and to estimate the CH4 magnitudes and model CH4 and δ13CH4 trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH4 trends for oil and gas were found to increase more than coal compared to the priors from 2000–2006 to 2007–2020. Estimated trends for coal emissions at 30∘ N–60∘ N are less than 50% of those from priors. Estimated global CH4 rice emissions trends are opposite to priors, with the largest contribution from the EQ to 60∘ N. The results of this study indicate that optimizing wetland emissions separately produces better agreement with the observed δ13CH4 trend than optimizing all biogenic emissions simultaneously. This study recommends optimizing separately biogenic emissions with similar isotopic signature to wetland emissions. In addition, this study suggests that fossil-based emissions were overestimated by 9% after 2012 and biogenic emissions are underestimated by 8% in the inversion using EDGAR v6.0 as priors.
