Second, we broadly aggregate carbon densities of different types of vegetation at the global level (see Methods). Local effects (e.g., of fire or drought if represented in DGVMs17), latitudinal differences in the effects of CO2 and temperature on the carbon sink35,36, and natural climate variability may thus be underrepresented in our estimates. This could explain distinct regional discrepancies between SLAND,TRENDY and SLAND,pi, particularly in forested regions (Supplementary Fig. 11). Third, potential errors in the LULUCF data need to be considered37,38,39, as they likely contribute to the supposed regional hotspots of emissions (Fig. 1c, Supplementary Figs. 2 and 7). For example, ref. 37 found a bias between observed biomass estimates and those simulated by BLUE in south Asia, Southeast Asia, and Equatorial Africa and attributed this bias bookkeeping model to an overestimation of prescribed wood harvest and clearing rates in the LULUCF data.
- The first column gives the abbreviation of the experiment type described in the second column, and the last three columns provide reference simulations for the uncertainty analysis (more information in Fig. 3).
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- The excess carbon pools are changed upon each land-use transition, whereby the spatially explicit actual woody biomass carbon densities derived from ref. 16 replace the woody biomass carbon densities based on ref. 17 from 2000 onward.
- In 850, the uncertainty around the baseline scenario is about 50 % for pasture and crop area, of which 1 % remain in 2014 (Fig. A1b).
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- The LASC term combines carbon fluxes from environmental changes on land that have been altered due to LULUCF and from changes in ELUC due to environmental effects (see Table 1).
1 Global FLUC
- Bookkeeping models typically use time-invariant carbon densities from inventories or models.
- Upon deforestation and wood harvest, this additional carbon is released and causes larger values of ELUC,trans compared to ELUC,pi.
- Importantly, annual updates to the LUH2 data, for use in the GCB, are provided when further/new information becomes available, and customised versions of the LUH2 data have been produced for use in specific studies (e.g. Frieler et al., 2017).
- Differences in initial land-cover distribution and transitions across different forcing datasets can also lead to substantial differences inestimated FLUC (Di Vittorio et al., 2020; Li et al., 2018; Gasser et al., 2020).
- Upon deforestation and wood harvest, the higher carbon stocks of vegetation and soil increase CO2 emissions in ELUC,trans compared to ELUC,pi by 24% (0.4 GtC yr−1) and 22% (0.3 GtC yr−1, Fig. 1b, Supplementary Figs. 5 and 6).
- Harvest is also the main driver of the asymmetry between cumulative net LULCC fluxes from HI/REG/LO scenarios after 1850.
The differences are likely explained by the substantial changes that came in with the change from LUH1 to LUH2 versions, in particular the change to Heinimann et al. (2017) shifting cultivation maps. Decreases in the RMSDHN-BLUE between SHNFull and SBL-Net globally and for 11 of the 18 regions (Fig. 4b), with small increases elsewhere. This shows that differences in setup and parameterisation cancel differences arising from the different land-use forcing in BLUE and HN2017 in some regions. In addition, the reductions in RMSDHN-BLUE in SHNFull compared to SBL are stronger than for SBL-Net, indicating that parameterisation differences have stronger contribution to RMSDHN-BLUE than the impact of simulation net/gross transitions.
Effect of transient land cover on the natural land sink
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- We choose a bookkeeping model in contrast to a DGVM because LULCC fluxes due to individual LULCC events can be traced and because of the potential to isolate the net LULCC flux independent of climate variability, among other factors (Pongratz et al., 2014).
- In the Hurtt et al. (2011) sensitivity study based on the LUH1 dataset (Chini et al., 2014), the authors analysed over 1600 simulations with respect to model “factors” like the simulation start date, the choice of historical and future agricultural land-use and wood harvest scenarios, and inclusion of shifting cultivation.
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- This highlights the importance of interactions between different parameters to the overall FLUC variability.
- The LUH2 dataset uses agricultural data from the uncertainty range A of the HYDE product, an uncertainty range based on literature and expert judgement.
Achieving a consistent estimate of the terrestrial carbon budget and its environmental and land-use components
This value is substantially outside the cumulative budget range of the GCB2019 (205±60 PgC 1850–2018) but still consistent with the uncertainty range of ±0.7 PgC yr−1 provided by GCB2019 after 1959. The artificial sensitivity experiments IC and Trans behave differently mainly in Trans, where for the period 1850–2014 the sensitivity of the cumulative net LULCC flux decreases with later starting year (Fig. 3), while over the full respective time periods the sensitivity increases with later starting year (not shown). Neglecting harvest and its uncertainty results in considerably reduced sensitivity to total LULCC uncertainty for simulations started in 1700 and 1850 (not shown). Interestingly, the reduction in cumulative net LULCC flux is largest in HI850NoH if considering the whole simulation (not shown), but from 1850 (Fig. 3), LO850NoH and REG850NoH show the largest reduction by omitting wood harvest. Our baseline scenario (REG1700) exhibits a cumulative net LULCC flux of 242 PgC for the period 1850–2014.
Model description of the Bookkeeping of Land Use Emissions model (BLUE)
For this purpose, we produced a time-series of the absolute carbon densities from DGVMs as input for BLUE to unearned revenue compare with the scaled BLUE carbon densities. Note that also in this sensitivity test we kept the ability of BLUE to account for degradation from primary to secondary land, which is usually lacking in DGVMs. To derive the carbon densities of secondary land in the sensitivity test, we multiply the DGVM carbon densities for primary land by the fraction of the secondary to primary carbon densities in BLUE. Figure 3Regional FLUC between 1850 and 2015 from the two BK model estimates in GCB2019 (HN2017 in black and SBL for BLUE in dark blue), the BLUE simulations with net LUC transitions and standard parameterisation (light blue, SBL-Net) and using HN2017 parameterisations (cyan, SHNFull). The factorial simulations with only one set of parameters changed are shown in thin lines (SHNCdens in dark red, SHNt in red, SHNAlloc in yellow).
