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

Ana Bastos,Tobias B Nützel,Kerstin Hartung,Julia Pongratz,Richard A Houghton,Julia E M S Nabel

doi:10.5194/esd-12-745-2021

Abstract

Abstract. Fluxes from deforestation, changes in land cover, land use and management practices (FLUC for simplicity) contributed to approximately 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 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 FLUC 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.

Highlights

Changes in land use and management are estimated to have contributed to a global source of CO2 to the atmosphere from the pre-industrial period until the present, and to account for more than 10 % of the total CO2 emissions over the past decade according to the Global Carbon Budget 2019 (Friedlingstein et al, 2019)
Several studies have shown that including management practices such as shifting cultivation, crop or wood harvesting might increase from land-use change and management (FLUC) by 70 % or more in individual dynamic global vegetation models (DGVMs) estimates (Arneth et al, 2017; Pugh et al, 2015) with management processes explaining some of the differences between biospheric fluxes from DGVMs and top-down estimates (Bastos et al, 2020)
Our analysis shows that FLUC estimates for these regions, except EU would be lower if the setup of HN2017 were used, i.e. starting in 1700 instead of 850 and using net transitions, and all four regions would show even larger reductions in FLUC if the parameterisation of HN2017 were used in BLUE

Summary

Introduction

Changes in land use and management are estimated to have contributed to a global source of CO2 to the atmosphere from the pre-industrial period until the present, and to account for more than 10 % of the total CO2 emissions over the past decade according to the Global Carbon Budget 2019 (Friedlingstein et al, 2019). Bastos et al.: Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterisation Reconstructing these changes consistently over the globe for the past centuries (let alone millennia) is, challenging and associated with high uncertainties (Hurtt et al, 2020; Klein Goldewijk et al, 2017; Pongratz et al, 2014; Ramankutty and Foley, 1999). The indirect environmental effects (accounted for in DGVMs but not in BK models) can be calculated by factorial simulations, in order to compare estimates from these two methods (Bastos et al, 2020) Whether and how these indirect effects are accounted for in FLUC creates large differences between estimates but can be resolved by a consistent terminology (Grassi et al, 2018; Pongratz et al, 2014). Several studies have shown that including management practices such as shifting cultivation, crop or wood harvesting might increase FLUC by 70 % or more in individual DGVM estimates (Arneth et al, 2017; Pugh et al, 2015) with management processes explaining some of the differences between biospheric fluxes from DGVMs and top-down estimates (Bastos et al, 2020)

Results

Discussion

Conclusion