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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 02 Jan 2020

Submitted as: model description paper | 02 Jan 2020

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This preprint is currently under review for the journal GMD.

PMIF v1.0: an inversion system to estimate the potential of satellite observations to monitor fossil fuel CO2 emissions over the globe

Yilong Wang1,2, Grégoire Broquet1, François-Marie Bréon1, Franck Lespinas1,3, Michael Buchwitz4, Maximilian Reuter4, Yasjka Meijer5, Armin Loescher5, Greet Janssens‑Maenhout6, Bo Zheng1, and Philippe Ciais1 Yilong Wang et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ-Université Paris Saclay, 91191, Gif-sur-Yvette CEDEX, France
  • 2The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • 3Canadian Centre for Meteorological and Environmental Prediction, 2121 Transcanada Highway, Dorval, QC, H9P 1J3, Canada
  • 4Institute of Environmental Physics (IUP), University of Bremen FB1, Otto Hahn Allee 1, 28334 Bremen, Germany
  • 5European Space Agency (ESA), Noordwijk, the Netherlands
  • 6European Commission, Joint Research Centre, Directorate Sustainable Resources, via E. Fermi 2749 (T.P. 123), 21027 Ispra, Italy

Abstract. This study assesses the potential of satellite imagery of vertically integrated columns of dry-air mole fractions of CO2 (XCO2) to constrain the emissions from cities and power plants (called emission clumps) over the whole globe during one year. The imagery is simulated for one imager of the Copernicus mission on Anthropogenic Carbon Dioxide Monitoring (CO2M) planned by the European Space Agency and the European Commission. The width of the swath of the CO2M instruments is about 300 km and the ground horizontal resolution is about 2 km resolution. A Plume Monitoring Inversion Framework (PMIF) is developed, relying on a Gaussian plume model to simulate the XCO2 plumes of each emission clump and on a combination of overlapping assimilation windows to solve for the inversion problem. The inversion solves for the 3 h mean emissions (during 8:30–11:30 local time) before satellite overpasses and for the mean emissions during other hours of the day (over the aggregation between 0:00–8:30 and 11:30–0:00) for each clump and for the 366 days of the year. Our analysis focuses on the derivation of the uncertainty in the inversion estimates (the “posterior uncertainty”) of the clump emissions. A comparison of the results obtained with PMIF and those from a previous study using a complex 3-D Eulerian transport model for a single city (Paris) shows that the PMIF system provides the correct order of magnitude for the uncertainty reduction of emission estimates (i.e. the relative difference between the prior and posterior uncertainties). Beyond the one or few large cities studied by previous studies, our results provide, for the first time, the global statistics of the uncertainty reduction of emissions for the full range of global clumps (differing in emission rate and spread, and distance from other major clumps) and meteorological conditions. We show that only the clumps with an annual emission budget higher than 2 MtC per year can potentially have their emissions between 8:30 and 11:30 constrained with a posterior uncertainty smaller than 20 % for more than 10 times within one year (ignoring the potential to cross or extrapolate information between 8:30–11:30 time windows on different days). The PMIF inversion results are also aggregated in time to investigate the potential of CO2M observations to constrain daily and annual emissions, relying on the extrapolation of information obtained for 8:30–11:30 time windows during days when clouds and aerosols do not mask the plumes, based on various assumptions regarding the temporal auto-correlations of the uncertainties in the emission estimates that are used as a prior knowledge in the Bayesian framework of PMIF. We show that the posterior uncertainties of daily and annual emissions are highly dependent on these temporal auto-correlations, stressing the need of systematic assessment of the sources of uncertainty in the spatiotemporally-resolved emission inventories used as prior estimates in the inversions. We highlight the difficulty to constrain global and national fossil fuel CO2 emissions with satellite XCO2 measurements only, and calls for integrated inversion systems that exploit multiple types of measurements.

Yilong Wang et al.

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Yilong Wang et al.

Yilong Wang et al.


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