Database of methane (CH4) and its stable carbon isotope (δ13C-CH4)

Database of methane (CH4) and its stable carbon isotope (δ13C-CH4) from atmospheric measurements compiled for CarbonTracker-CH4 2023: https://gml.noaa.gov/ccgg/carbontracker-ch4-2023/

Authors

Xin Lan*, Kenneth Schuldt*, Sylvia Englund Michel#, John Mund, Lori Bruhwiler, Youmi Oh, Sourish Basu, Kirk Thoning, Reid Clark, John B. Miller, Arlyn Andrews, Ed Dlugokencky, Pieter Tans, Tuula Aalto, Stefano Amendola, Sébastien Conil Andra, Marcos Andrade, Nhat Anh Nguyen, Shuji Aoki, Francesco Apadula, Ikhsan Buyung Arifin, Sabrina Arnold, Mikhail Arshinov, Bianca Baier, Peter Bergamaschi, Tobias Biermann, Sebastien C. Biraud, Pierre-Eric Blanc, Gordon Brailsford, Huilin Chen, Aurelie Colomb, Cedric Couret, Paolo Cristofanelli, Emilio Cuevas, ?ukasz Chmura, Marc Delmotte, Lukas Emmenegger, Gulzhan Esenzhanova, Ryo Fujita, Luciana Gatti, Elise-Andree Guerette, László Haszpra, Michal Heliasz, Ove Hermansen, Jutta Holst, Tatiana Di Iorio, Armin Jordan, Müller-Williams Jennifer, Anna Karion, Teruo Kawasaki, Victor Kazan, Petri Keronen, Seung-Yeon Kim, Tobias Kneuer, Katerina Kominkova, Elena Kozlova, Paul Krummel, Dagmar Kubistin, Casper Labuschagne, Ray Langenfelds, Olivier Laurent, Tuomas Laurila, Haeyoung Lee, Irene Lehner, Markus Leuenberger, Matthias Lindauer, Morgan Lopez, Reza Mahdi, Ivan Mammarella, Giovanni Manca, Michal V. Marek, Martine De Mazière, Kathryn McKain, Frank Meinhardt, Charles E. Miller, Meelis Mölder, John Moncrieff, Heiko Moosen, Caisa Moreno, Shinji Morimoto, Catherine Lund Myhre, Alberth Christian Nahas, Jaroslaw Necki, Sylvia Nichol, Simon O'Doherty, Nina Paramonova, Salvatore Piacentino, Jean Marc Pichon, Christian Plass-Dülmer, Michel Ramonet, Ludwig Ries, Alcide Giorgio di Sarra, Motoki Sasakawa, Daniel Say, Hinrich Schaefer, Bert Scheeren, Martina Schmidt, Marcus Schumacher, Mahesh Kumar Sha, Paul Shepson, Dan Smale, Paul D. Smith, Martin Steinbacher, Colm Sweeney, Shinya Takatsuji, Gaston Torres, Kjetil Tørseth, Pamela Trisolino, Jocelyn Turnbull, Karin Uhse, Taku Umezawa, Alex Vermeulen, Isaac Vimont, Gabriela Vitkova, Hsiang Jui Ray Wang, Doug Worthy, and Irène Xueref-Remy.

* Primary contact for CH4 data: Xin.Lan@noaa.gov and Kenneth.Schuldt@noaa.gov

# Primary contact for d13CH4 data: Sylvia.Michel@colorado.edu

Overview

For NOAA’s global methane assimilation system, CarbonTracker-CH4, we used an extensive atmospheric measurement database of atmospheric CH4 and its stable isotope ratios (δ13C-CH4) to best inform global CH4 emissions.

To maximize the spatiotemporal coverage of in-situ CH4 and δ13C-CH4 data, we compiled a new atmospheric measurement database by harmonizing measurements from NOAA/INSTAAR (Institute of Arctic and Alpine Research of University of Colorado Boulder) with another 29 research laboratories around the world.

All CH4 data were quality checked, converted to a common CH4 standard scale (the World Meteorological Organization X2004A scale maintained by NOAA (Dlugokencky et al., 2005)), and assigned with lab-specific measurement uncertainty.

In CarbonTracker-CH4, We converted all data to the WMO X2004A scale by applying a scale multiplier to data that were not on WMO X2004A scale. We used an inversion scheme that requires a measurement uncertainty for each observation. Although uncertainties had been assigned to NOAA/GML CH4 data, many data sets shared for this project had not. So, we applied the same fundamental approach used for NOAA/GML uncertainties to other data sets.

Measurement Uncertainty

For GML data, uncertainties are calculated for each measurement based on analytical repeatability, reproducibility, our ability to propagate the WMO CH4 mole fraction standard scale, and additional terms, if necessary. Analytical repeatability, or short-term measurement noise (sometimes called precision), is assessed through a number of methods, but most commonly as the standard deviation of the mean of multiple measurements of natural air from a cylinder. It varies with analytical instruments from 0.3 to 2.3 ppb (all uncertainties are given as 68% confidence intervals). Propagation of the scale is based on the reproducibility determined for scale propagation in our calibration laboratory. It has a fixed value of 0.5 ppb based on repeat calibrations of the same cylinder at least one year after the first. Reproducibility is based on long-term measurement variations of target cylinders, typically ~0.3 ppb. The three terms are added in quadrature (square root of the sum of the squares) to estimate the measurement uncertainty at a 68% confidence interval.

For non-GML measurements, we used the uncertainty assigned by the data provider (if available), or we estimated uncertainties using the same approach used for GML measurements. Uncertainty terms were assessed from publicly available information (publications, meta data, etc.), if available, or from an informed guess based on GML experience with the analytical method used. These uncertainties include additional terms as necessary (e.g., for internal scale propagation, conversion from a different scale, etc.).

We used δ13C-CH4 data from INSTAAR as well as other isotope laboratories making precise measurements of atmospheric CH4 with isotope ratio mass spectrometers. The INSTAAR δ13C-CH4 data were measured in a subset of air samples collected from NOAA/GML's Global Greenhouse Gas Reference Network (GGGRN). Because different labs have independent ties to primary reference materials which do not agree, we calculated offsets to bring the δ13C-CH4 data onto the INSTAAR realization of the Vienna Pee Dee Belemnite (VPDB) scale (Miller et al., 2002). These offsets were based on measurements of air directly from cylinders, flasks filled with air from cylinders, or co-located sample data, and are all described in Umezawa et al. (2018). When there was not a direct comparison, e.g., between INSTAAR and NIES, or INSTAAR and NIPR, we used comparisons between each of these labs and the Institute for Marine and Atmospheric research Utrecht (IMAU). Each comparison has an uncertainty associated with it, which were combined in quadrature to account for uncertainty in the offset correction. The total uncertainty on assimilated δ13CH4 measurements was typically less than 0.15 ‰.

Product Information

This version supersedes the previous version of this database (https://doi.org/10.15138/64w0-0g71) with extended CH4 and δ13C-CH4 data, and additional information of metadata.

Datasets for CH4 include CH4 Info. Note that to bring all CH4 data to the WMO X2004A scale, multiply the "value" in the Variables field by the "ch4_scale_multiplier" in the Global Attributes for each nc file.

Datasets for δ13C-CH4 include δ13C-CH4 Info. Note that to bring all δ13C-CH4 data to the INSTAAR scale, subtract the "ch4c13_scale_offset" in the Global Attributes from the "value" in the Variables field for each nc file.

References of Datasets Included in This Database:

Lan, X., E.J. Dlugokencky, J.W. Mund, A.M. Crotwell, M.J. Crotwell, E. Moglia, M. Madronich, D. Neff and K.W. Thoning (2023), Atmospheric Methane Dry Air Mole Fractions from the NOAA GML Carbon Cycle Cooperative Global Air Sampling Network, 1983-2021, Version: 2023-08-28, https://doi.org/10.15138/VNCZ-M766

Lan, X., Dlugokencky, E.J., A.M. Crotwell, K.W. Thoning, and J.W. Mund (2023), Atmospheric methane from quasi-continuous measurements at Barrow, Alaska and Mauna Loa, Hawaii, 1986-2022, Version: 2023-08, https://doi.org/10.15138/ve0c-be70

Michel, E. S., B. H. Vaughn, P. Tans, K. Thoning, X. Lan. Atmospheric δ13C-CH4 data from the Institute of Arctic and Alpine Research (INSTAAR) at the University of Colorado, Boulder in cooperation with NOAA Global Monitoring Laboratory, 2021, https://doi.org/10.15138/79jq-qc24

Fair Use Statement

This database is made freely available to the scientific community and is intended to stimulate and support global methane cycle and other modeling studies. We rely on the ethics and integrity of the user to assure that the authors receive fair credit for their work. Fair credit will depend on the nature of the work and the requirements of the institutions involved. Your use of this database implies an agreement to contact the database co-authors to discuss the nature of the work and the appropriate level of acknowledgement. If the database is essential to the work, or if an important result or conclusion depends on the database, co-authorship may be appropriate. This should be discussed with the co-authors at an early stage in the work. Contacting the co- authors is not optional; if you use the database, you must contact the co-authors. A co-author email distribution list is provided during the database download process, which generates an automated e-mail to the user containing all relevant information.

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