Supporting code for manuscript:
Georgiou K., Koven C. D., Wieder W. R., Hartman M. D., Riley W. J., Pett-Ridge J., Bouskill N. J., Abramoff R. Z., Slessarev E., Ahlström A., Parton W. J., Pellegrini A. F. A., Pierson D., Sulman B. N., Zhu Q., Jackson R. B. Emergent temperature sensitivity of soil organic carbon driven by mineral associations. Nature Geoscience, 2024.
https://www.nature.com/articles/s41561-024-01384-7
In this manuscript, we analyzed (i) an observationally-derived global data product and (ii) model output from CMIP6 Earth system models and offline land models to quantify the distribution of carbon between underlying soil organic matter pools and their respective climatological temperature sensitivities. Here we provide the R and Python scripts to perform the analyses.
A permanent DOI for this Github entry was created through Zenodo:
All global datasets are freely available at the links and references provided, and are also available upon request.
CMIP6 ESMs: https://esgf-node.llnl.gov/search/cmip6/
Biogeochemical Testbed: https://doi.org/10.5065/d6nc600w
Data Product: https://doi.org/10.5281/zenodo.6539765
The provided scripts were run with Python version 3.10 and R version 4.3.2.