Preprints
https://doi.org/10.5194/egusphere-2023-2025
https://doi.org/10.5194/egusphere-2023-2025
26 Jan 2024
 | 26 Jan 2024
Status: this preprint is open for discussion.

Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and the impact on a multi-species inversion with GEOS-Chem (v12.5)

Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer

Abstract. Monitoring, Reporting and Verification (MRV) frameworks for greenhouse gas (GHG) emissions are being developed by countries across the world to keep track of progress towards national emission reduction targets. Data assimilation plays an important role in MRVs, combining different sources of information to get the best possible estimate of fossil fuel emissions and as a consequence better estimates for fluxes from the natural biosphere. Robust estimates for fossil fuel emissions rely on accurate estimates of uncertainties corresponding to the different pieces of information. We describe prior uncertainties in CO2 and CO fossil fuel fluxes, with special attention paid to spatial error correlations and the covariance structure between CO2 and CO. This represents the first time that the prior uncertainties in CO2 and the important co-emitted trace gas CO are defined consistently, including error correlations, which allows us to make use of the synergy between the two trace gases to better constrain CO2 fossil fuel fluxes. The CO:CO2 error correlations differ per sector, depending on the diversity of sub-processes occurring within a sector, and also show a large range in values between pixels for the same sector. For example, for other stationary combustion the pixel correlation values range from 0.1 to 1.0, whereas for road transport the correlation is mostly larger than 0.6. We illustrate the added value of our prior uncertainty definition using closed-loop numerical experiments over mainland Europe and the UK, which isolate the influence of using error correlations between CO2 and CO and the influence of prescribing more detailed information about prior emission uncertainties. We find that using our realistic prior uncertainty definition helps our data assimilation system to differentiate more easily between CO2 fluxes from biogenic and fossil fuel sources. Using the improved prior emission uncertainties we find fewer geographic regions with significant changes from the prior than using the default prior uncertainties, but they almost consistently move closer to the prescribed true values, in contrast to the default prior uncertainties. We also find that using CO provides additional information on CO2 fossil fuel fluxes, but only if the CO:CO2 error covariance structure is defined realistically. Using the default prior uncertainties, the CO2 fossil fuel fluxes move farther away from the truth for many geographical regions. With the default uncertainties the maximum deviation of fossil fuel CO2 from the prescribed truth is about 7 % in both the prior and posterior result. With the advanced uncertainties this is reduced to 3 % in the posterior.

Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer

Status: open (until 23 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer

Data sets

Data and code related to manuscript 'Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and the impact on a multi-species inversion with GEOS-Chem (v12.5)' I. Super, T. Scarpelli, A. Droste, and P. Palmer https://doi.org/10.5281/zenodo.10554686

Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer

Viewed

Total article views: 141 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
101 37 3 141 19 3 1
  • HTML: 101
  • PDF: 37
  • XML: 3
  • Total: 141
  • Supplement: 19
  • BibTeX: 3
  • EndNote: 1
Views and downloads (calculated since 26 Jan 2024)
Cumulative views and downloads (calculated since 26 Jan 2024)

Viewed (geographical distribution)

Total article views: 143 (including HTML, PDF, and XML) Thereof 143 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 21 Feb 2024
Download
Short summary
Monitoring greenhouse gas emission reductions requires a combination of models, observations and a first estimate of the emissions. Each of these components provides information with a certain level of certainty and is weighted to get the most reliable estimate of actual emissions. We describe efforts to estimate the uncertainty in the first emission estimate, which has a significant impact on the outcome. Hence, a good uncertainty estimate is important to get reliable information on emissions.