Articles | Volume 17, issue 15
Biogeosciences, 17, 3961–3989, 2020
https://doi.org/10.5194/bg-17-3961-2020
Biogeosciences, 17, 3961–3989, 2020
https://doi.org/10.5194/bg-17-3961-2020

Research article 05 Aug 2020

Research article | 05 Aug 2020

Understanding the uncertainty in global forest carbon turnover

Thomas A. M. Pugh et al.

Data sets

Simulations from the JULES dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison A. Harper https://doi.org/10.5281/zenodo.3579375

Simulations from the CABLE-POP land surface model for the Vegetation Carbon Turnover Intercomparison V. Haverd https://doi.org/10.5281/zenodo.3579407

Simulations from the LPJmL3.5 dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison J. Heinke, A. Rammig, and K. Thonicke https://doi.org/10.5281/zenodo.3579396

Simulations from the ORCHIDEE dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison J. Barichivich https://doi.org/10.5281/zenodo.3579402

Simulations from the SEIB-DGVM dynamic global vegetation model for the Vegetation Carbon Turnover Intercomparison H. Sato and K. Nishina https://doi.org/10.5281/zenodo.3579384

Simulations from the LPJ-GUESS dynamic global vegetation model v3.0 for the Vegetation Carbon Turnover Intercomparison T. A. M. Pugh and B. Beckage https://doi.org/10.5281/zenodo.3576036

Model code and software

pughtam/turnover_comp: Code for "Understanding the uncertainty in global forest carbon turnover" T. A. M. Pugh, T. Rademacher, S. L. Shafer, and J. Steinkamp https://doi.org/10.5281/zenodo.3907757

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Short summary
The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle. Estimates from six contemporary models found this time to range from 12.2 to 23.5 years for the global mean for 1985–2014. Future projections do not give consistent results, but 13 model-based hypotheses are identified, along with recommendations for pragmatic steps to test them using existing and novel observations, which would help to reduce large current uncertainty.
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