Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

Journal metrics

  • IF value: 10.951 IF 10.951
  • IF 5-year value: 9.899 IF 5-year
    9.899
  • CiteScore value: 9.74 CiteScore
    9.74
  • SNIP value: 3.111 SNIP 3.111
  • IPP value: 8.99 IPP 8.99
  • SJR value: 5.229 SJR 5.229
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 38 Scimago H
    index 38
  • h5-index value: 33 h5-index 33
Discussion papers
https://doi.org/10.5194/essd-2019-61
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-61
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data description paper 09 May 2019

Data description paper | 09 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

Global atmospheric carbon monoxide budget 2000–2017 inferred from multi-species atmospheric inversions

Bo Zheng1, Frederic Chevallier1, Yi Yin2, Philippe Ciais1, Audrey Fortems-Cheiney1, Merritt N. Deeter3, Robert J. Parker4,5, Yilong Wang1, Helen M. Worden3, and Yuanhong Zhao1 Bo Zheng et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, France
  • 2Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
  • 3Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
  • 4Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UK
  • 5National Centre for Earth Observation, University of Leicester, Leicester, UK

Abstract. Atmospheric carbon monoxide (CO) concentrations have been decreasing since 2000 as observed by both satellite- and ground-based instruments, but global bottom-up emission inventories surprisingly estimate increasing anthropogenic CO emissions concurrently. In this study, we use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric CO variations to its sources and sinks in order to achieve a full closure of the global CO budget during 2000–2017. Our observation constraints include satellite retrievals of the total column mole fraction of CO, formaldehyde (HCHO), and methane (CH4) that are all major components of the atmospheric CO cycle. Three inversions (i.e., 2000–2017, 2005–2017, and 2010–2017) are performed to use the observation data to the maximum extent possible as they become available and assess the consistency of inversion results to the assimilation of more trace gas species. We identify a declining trend in the global CO budget since 2000 (three inversions are broadly consistent during overlapping periods), driven by reduced anthropogenic emissions in the U.S. and Europe (both likely from the transport sector), and in China (likely from industry and residential sectors), as well as by reduced biomass burning emissions globally, especially in Equatorial Africa (associated with reduced burned areas). We show that the trends and drivers of the inversion-based CO budget are not affected by the inter-annual variation assumed for prior CO fluxes. All three inversions estimate that surface CO emissions contradict the global bottom-up inventories in the world's top two emitters for the sign of anthropogenic emission trends in China (e.g., here −0.8 ± 0.5 % yr−1 since 2000 while the prior gives 1.3 ± 0.4 % yr−1) and for the rate of anthropogenic emission increase in South Asia (e.g., here 1.0 ± 0.6 % yr−1 since 2000 smaller than 3.5 ± 0.4 % yr−1 in the prior inventory). The posterior model CO concentrations and trends agree well with independent ground-based observations and correct the prior model bias. The comparison of the three inversions with different observation constraints further suggests that the most complete constrained inversion that assimilates CO, HCHO, and CH4 has a good representation of the global CO budget, therefore matches best with independent observations, while the inversion only assimilating CO tends to underestimate both the decrease in anthropogenic CO emissions and the increase in the CO chemical production. The global CO budget data from all three inversions in this study can be accessed from https://doi.org/10.6084/m9.figshare.c.4454453.v1 (Zheng et al., 2019).

Bo Zheng et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Topical Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Bo Zheng et al.
Data sets

Global atmospheric carbon monoxide budget 2000–2017 B. Zheng, F. Chevallier, and P. Ciais https://doi.org/10.6084/m9.figshare.c.4454453.v1

Bo Zheng et al.
Viewed  
Total article views: 372 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
263 106 3 372 24 3 3
  • HTML: 263
  • PDF: 106
  • XML: 3
  • Total: 372
  • Supplement: 24
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 09 May 2019)
Cumulative views and downloads (calculated since 09 May 2019)
Viewed (geographical distribution)  
Total article views: 325 (including HTML, PDF, and XML) Thereof 322 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 17 Jul 2019
Download
Short summary
We use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric carbon monoxide (CO) variations to its sources and sinks in order to achieve a full closure of the global CO budget during 2000–2017. We identify a declining trend in the global CO budget since 2000, driven by reduced anthropogenic emissions in the U.S., Europe, and China, as well as by reduced biomass burning emissions globally, especially in Equatorial Africa.
We use a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed...
Citation