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Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterization. In open review for Earth System Dynamics

MPS-Authors

Bastos,  Ana
Max Planck Institute for Biogeochemistry, Max Planck Society;

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Nabel,  Julia E. M. S.
Emmy Noether Junior Research Group Forest Management in the Earth System, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

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Pongratz,  Julia
Emmy Noether Junior Research Group Forest Management in the Earth System, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

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Citation

Bastos, A., Hartung, K., Nützel, T. B., Nabel, J. E. M. S., Houghton, R. A., & Pongratz, J. (submitted). Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterization. In open review for Earth System Dynamics.


Cite as: http://hdl.handle.net/21.11116/0000-0007-B9C8-7
Abstract
Fluxes from deforestation, changes in land-cover, land-use and management practices (FLUC for simplicity) contributed to circa 14 % of anthropogenic CO2 emissions in 2009–2018. Estimating FLUC accurately in space and in time remains, however, challenging, due to multiple sources of uncertainty in the calculation of these fluxes. This uncertainty, in turn, is propagated to global and regional carbon budget estimates, hindering the compilation of a consistent carbon budget and preventing us from constraining other terms, such as the natural land sink. Uncertainties in FLUC estimates arise from many different sources, including differences in model structure (e.g., process- based vs. bookkeeping) and model parameterization. Quantifying the uncertainties from each source requires controlled simulations to separate their effects. Here we analyze differences between the two bookkeeping models used regularly in the global carbon budget estimates since 2017: the model by Hansis et al. (Hansis et al., 2015) (BLUE) and that by Houghton and Nassikas (Houghton and Nassikas, 2017) (HN2017). The two models have a very similar structure and philosophy, but differ significantly both with respect to FLUC intensity and spatio-temporal variability. This is due to differences in the land-use forcing, but also in the model parameterization. We find that the larger emissions in BLUE compared to HN2017 are largely due to differences in C densities between natural and managed vegetation or primary and secondary vegetation, and higher allocation of cleared and harvested material to fast turnover pools in BLUE than in HN2017. Beside parameterization and the use of different forcing, other model assumptions cause differences, in particular that BLUE represents gross transitions which leads to overall higher carbon losses that are also more quickly realized than HN2017