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

Data sets

TimeFRAME E. J. Harris and P. Fischer https://gitlab.renkulab.io/eliza.harris/timeframe

TimeFRAME E. J. Harris and P. Fischer https://github.com/elizaharris/TimeFRAME

Model code and software

TimeFRAME: Time-dependent Fractionation and Mixing Evaluation. Contains source files and notebooks for the development of N2O pathway quantification methods P. Fischer https://renkulab.io/projects/fischphi/n2o-pathway-analysis

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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.
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