Articles | Volume 15, issue 9
https://doi.org/10.5194/bg-15-2909-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/bg-15-2909-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions
Sebastian Lienert
CORRESPONDING AUTHOR
Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Fortunat Joos
Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
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Latest update: 23 Nov 2024
Short summary
Deforestation, shifting cultivation and wood harvesting cause large carbon emissions, altering climate. We apply a dynamic global vegetation model in a probabilistic framework. Diverse observations are assimilated to establish an optimally performing model and a large ensemble of model versions. Land-use carbon emissions are reported for individual countries, regions and the world. We find that parameter-related uncertainties are on the same order of magnitude as process-related effects.
Deforestation, shifting cultivation and wood harvesting cause large carbon emissions, altering...
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