Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers

De Pue, Jan; Wieneke, Sebastian; Bastos, Ana; Barrios, José Miguel; Liu, Liyang; Ciais, Philippe; Arboleda, Alirio; Hamdi, Rafiq; Maleki, Maral; Maignan, Fabienne; Gellens-Meulenberghs, Françoise; Janssens, Ivan; Balzarolo, Manuela

doi:https://doi.org/10.5194/bg-20-4795-2023

Articles | Volume 20, issue 23

https://doi.org/10.5194/bg-20-4795-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-20-4795-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 23

Research article

|

06 Dec 2023

Research article |

| 06 Dec 2023

Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers

Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Gellens-Meulenberghs, Ivan Janssens, and Manuela Balzarolo

Supplement

https://doi.org/10.5194/bg-20-4795-2023-supplement

Data sets

Observed and modelled GPP at 61 eddy covariance sites (2007-2018) Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Meulenberghs, Ivan Janssens, and Manuela Balzarolo https://doi.org/10.5281/zenodo.7928514

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Short summary

The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. To estimate this flux, models can rely on remote sensing data (RS-driven), meteorological data (meteo-driven) or a combination of both (hybrid). An intercomparison of 11 models demonstrated that RS-driven models lack the sensitivity to short-term anomalies. Conversely, the simulation of soil moisture dynamics and stress response remains a challenge in meteo-driven models.