4.5 Article

Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterisation

Journal

EARTH SYSTEM DYNAMICS
Volume 12, Issue 2, Pages 745-762

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/esd-12-745-2021

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Fluxes from deforestation, land cover changes, and land use practices contributed to approximately 14% of anthropogenic CO2 emissions in 2009-2018. However, accurately estimating F-LUC remains challenging due to uncertainties in calculation methods. Differences between two bookkeeping models (BLUE and HN2017) used in global carbon budget estimates since 2017 are mainly attributed to variations in land-use forcing and model parameterization, affecting carbon emissions intensity and spatial-temporal variability.
Fluxes from deforestation, changes in land cover, land use and management practices (F-LUC for simplicity) contributed to approximately 14% of anthropogenic CO2 emissions in 2009-2018. Estimating F-LUC 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 F-LUC estimates arise from many different sources, including differences in model structure (e.g. process based vs. bookkeeping) and model parameterisation. Quantifying the uncertainties from each source requires controlled simulations to separate their effects. Here, we analyse differences between the two bookkeeping models used regularly in the global carbon budget estimates since 2017: the model by Hansis et al. (2015) (BLUE) and that by Houghton and Nassikas (2017) (HN2017). The two models have a very similar structure and philosophy, but differ significantly both with respect to F-LUC intensity and spatiotemporal variability. This is due to differences in the land-use forcing but also in the model parameterisation. 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. Besides parameterisation 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 realised than HN2017.

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