Ecosystem physio-phenology revealed using circular statistics

Pabon-Moreno, Daniel E.; Musavi, Talie; Migliavacca, Mirco; Reichstein, Markus; Römermann, Christine; Mahecha, Miguel D.

doi:https://doi.org/10.5194/bg-17-3991-2020

Articles | Volume 17, issue 15

https://doi.org/10.5194/bg-17-3991-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-17-3991-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 15

Research article

|

06 Aug 2020

Research article |

| 06 Aug 2020

Ecosystem physio-phenology revealed using circular statistics

Daniel E. Pabon-Moreno, Talie Musavi, Mirco Migliavacca, Markus Reichstein, Christine Römermann, and Miguel D. Mahecha

Supplement

https://doi.org/10.5194/bg-17-3991-2020-supplement

Data sets

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data Gilberto Pastorello, Carlo Trotta, and Eleonora Canfora et al. https://doi.org/10.1038/s41597-020-0534-3

Model code and software

Code-repository for the research paper: "Ecosystem Physio-phenology revealed using circular statistics" Daniel E. Pabon-Moreno, Talie Musavi, Mirco Migliavacca, Markus Reichstein, Christine Römermann, and Miguel D. Mahecha https://doi.org/10.5281/zenodo.3921892

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Short summary

Ecosystem CO₂ uptake changes in time depending on climate conditions. In this study, we analyze how different climate variables affect the timing when CO₂ uptake is at a maximum (DOY_GPPmax). We found that the joint effects of radiation, temperature, and vapor pressure deficit are the most relevant controlling factors of DOY_GPPmax and that if they increase, DOY_GPPmax will happen earlier. These results help us to better understand how CO₂ uptake could be affected by climate change.