Articles | Volume 21, issue 16
https://doi.org/10.5194/bg-21-3641-2024
https://doi.org/10.5194/bg-21-3641-2024
Technical note
 | 
22 Aug 2024
Technical note |  | 22 Aug 2024

Technical note: A Bayesian mixing model to unravel isotopic data and quantify trace gas production and consumption pathways for time series data – Time-resolved FRactionation And Mixing Evaluation (TimeFRAME)

Eliza Harris, Philipp Fischer, Maciej P. Lewicki, Dominika Lewicka-Szczebak, Stephen J. Harris, and Fernando Perez-Cruz

Viewed

Total article views: 988 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
777 164 47 988 25 34 33
  • HTML: 777
  • PDF: 164
  • XML: 47
  • Total: 988
  • Supplement: 25
  • BibTeX: 34
  • EndNote: 33
Views and downloads (calculated since 29 Nov 2023)
Cumulative views and downloads (calculated since 29 Nov 2023)

Viewed (geographical distribution)

Total article views: 988 (including HTML, PDF, and XML) Thereof 1,024 with geography defined and -36 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
Greenhouse gases are produced and consumed via a number of pathways. Quantifying these pathways helps reduce the climate and environmental footprint of anthropogenic activities. The contribution of the pathways can be estimated from the isotopic composition, which acts as a fingerprint for these pathways. We have developed the Time-resolved FRactionation And Mixing Evaluation (TimeFRAME) model to simplify interpretation and estimate the contribution of different pathways and their uncertainty.
Altmetrics
Final-revised paper
Preprint