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The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates

Worden, John R. and Cusworth, Daniel H. and Qu, Zhen and Yin, Yi and Zhang, Yuzhong and Bloom, A. Anthony and Ma, Shuang and Byrne, Brendan K. and Scarpelli, Tia and Maasakkers, Joannes D. and Crisp, David and Duren, Riley and Jacob, Daniel J. (2022) The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates. Atmospheric Chemistry and Physics, 22 (10). pp. 6811-6841. ISSN 1680-7324. doi:10.5194/acp-22-6811-2022. https://resolver.caltech.edu/CaltechAUTHORS:20220721-8096000

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Abstract

We use optimal estimation (OE) to quantify methane fluxes based on total column CH₄ data from the Greenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We then project these fluxes to emissions by sector at 1∘ resolution and then to each country using a new Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. These estimates are intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are ∼ 263 ± 24 Tg CH₄ yr⁻¹, anthropogenic fossil emissions are 82 ± 12 Tg CH₄ yr⁻¹, and natural wetland/aquatic emissions are 180 ± 10 Tg CH₄ yr⁻¹. These estimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In addition, anthropogenic fossil estimates are consistent with those reported to the United Nations Framework Convention on Climate Change (80.4 Tg CH₄ yr⁻¹ for 2019). Alternative priors can be easily tested with our new Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on isotopic evidence) and find little impact on our posterior fluxes. This indicates that these alternative inventories are inconsistent with our remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total emissions for approximately 57 countries can be resolved with this observing system based on the degrees-of-freedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of prior inventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different (although within uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicating a need for further studies reconciling these different approaches for quantifying the methane budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO₂M, MethaneSat, or Carbon Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.5194/acp-22-6811-2022DOIArticle
https://cmsflux.jpl.nasa.govRelated ItemData
ORCID:
AuthorORCID
Worden, John R.0000-0003-0257-9549
Cusworth, Daniel H.0000-0003-0158-977X
Qu, Zhen0000-0002-3766-9838
Yin, Yi0000-0003-4750-4997
Zhang, Yuzhong0000-0001-5431-5022
Bloom, A. Anthony0000-0002-1486-1499
Ma, Shuang0000-0002-6494-724X
Byrne, Brendan K.0000-0003-0619-3045
Maasakkers, Joannes D.0000-0001-8118-0311
Crisp, David0000-0002-4573-9998
Duren, Riley0000-0003-4723-5280
Jacob, Daniel J.0000-0002-6373-3100
Additional Information:© Author(s) 2022. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 16 Nov 2021 – Discussion started: 07 Dec 2021 – Revised: 05 Apr 2022 – Accepted: 01 May 2022 – Published: 25 May 2022. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA; 80NM0018D0004). This research was motivated by CEOS (Committee on Earth Observing Satellites) activities related to quantifying greenhouse gas emissions. This research was supported by funding from NASA's Carbon Monitoring System (CMS) and AIST programs. Yuzhong Zhang was funded by the NSFC (42007198). This research has been supported by the National Aeronautics and Space Administration, Science Mission Directorate (grant no. 18-CMS18-0018). Author contributions. JRW led the integration of results and writing and developed the prior covariances. DHC provided the emissions attribution with JRW and AB and co-wrote Sect. 2.2. ZQ and YZ provided the flux estimates and co-wrote Sect. 2.1. YY, SM, and AAB supported the attribution derivation and analysis. BKB and DC helped link results to the global stock take. TS and JDM supported the inventory description and analysis. RD and DJJ helped design the overall flux inversion and emissions attribution system described in the paper. All the co-authors have read the paper and provided feedback. Data availability. The prior and posterior emissions and covariances are stored on https://cmsflux.jpl.nasa.gov/ (last access: 21 May 2022). Please refer to Qu et al. (2021) for data related to the top-down flux inversion. The provenance of individual inventories that are used to generate the emissions and inventories are shown in Table 2. The contact author has declared that neither they nor their co-authors have any competing interests. Review statement. This paper was edited by Bryan N. Duncan and reviewed by two anonymous referees.
Funders:
Funding AgencyGrant Number
NASA/JPL/Caltech80NM0018D0004
National Natural Science Foundation of China42007198
NASA18-CMS18-0018
Issue or Number:10
DOI:10.5194/acp-22-6811-2022
Record Number:CaltechAUTHORS:20220721-8096000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220721-8096000
Official Citation:Worden, J. R., Cusworth, D. H., Qu, Z., Yin, Y., Zhang, Y., Bloom, A. A., Ma, S., Byrne, B. K., Scarpelli, T., Maasakkers, J. D., Crisp, D., Duren, R., and Jacob, D. J.: The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates, Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, 2022.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115722
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:26 Jul 2022 17:04
Last Modified:26 Jul 2022 17:04

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