Net land-use change carbon flux estimates and sensitivities – An assessment with a bookkeeping model based on CMIP6 forcing

Abstract

Abstract. The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (BLUE) based on a LULCC dataset including uncertainty estimates (the LUH2 dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low land- use estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %, reference are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than from LULCC uncertainty and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr−1 at an estimate of 1.7 PgC yr−1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor three) than the other two factors for the net LULCC flux in 2014 and historical LULCC uncertainty is negligible for estimates of future scenarios.

Land Use Effects on Climate: Current State, Recent Progress, and Emerging Topics

“…Additional uncertainties arise from the translation of land use data into land cover conversion [89]. As land use maps are used in DGVMs, bookkeeping models, and satellite-based stock change approaches, the associated uncertainties in land use patterns are propagated to the majority of GHG flux estimations (Table 1 [28,90]). ( 2) The degree of implementation of LULCC practices (e.g., degradation, drainage, grazing, irrigation, shifting cultivation, wood harvest) varies across models as highlighted in Fig.…”

“…( 2) The degree of implementation of LULCC practices (e.g., degradation, drainage, grazing, irrigation, shifting cultivation, wood harvest) varies across models as highlighted in Fig. 1 and Table 1 [10, [90][91][92][93], and practices can be implemented with very different complexity and process realism [2,94]. (3) LULCC CO 2 flux estimates are very sensitive to model parameterizations that are often not well constrained by observational data and differ between models, such as carbon densities in bookkeeping models and allocation of wood to slash and product pools of different lifetimes (Table 1, e.g., [95,96]).…”

A comprehensive dataset for global, regional and national greenhouse gas emissions by sector 1970–2019

“…In particular land management, such as crop harvesting, tillage, or grazing (all implicitly included in observation-based carbon densities of bookkeeping models) can alter CO2 flux estimates substantially, but are included to varying extents in DGVMs, thus increasing model spread (Arneth et al, 2017). For all types of models, land-use forcing is a major determinant of emissions and removals, and its high uncertainty impacts CO2-LULUCF estimates (Hartung et al, 2021). The reconstruction of land-use change of the historical past, which has to cover decades to centuries of legacy LULUCF fluxes, is based on sparse data or proxies (Hurtt et al, 2020;Klein Goldewijk et al, 2017), while satellite-based products suffer from complications in distinguishing natural from anthropogenic drivers (Hansen et al, 2013;Li et al, 2018) or accounting for small-scale disturbances and degradation (Matricardi et al, 2020).…”