Figure A2Global gross transitions based on LUH2 baseline scenario (REG) (a) and absolute difference of high (HI) and low (LO) land-use estimates compared to the baseline LUH2 setup (b–e). Our baseline scenario (REG1700) exhibits a cumulative net LULCC flux of 242 PgC for the period 1850–2014. The sensitivity range due to LULCC uncertainty and starting year is about 22 % for comparable setups. In the nine main experiments, the cumulative net LULCC flux is at least 201 PgC (HI850) and at most 264 PgC (LO1850). The relative change due to neglecting gross transitions is similar across LULCC setups, and for REG1700net the cumulative net LULCC flux is reduced to 211 PgC. Wood harvest causes the largest sensitivity in the cumulative net LULCC flux (the flux in REG1700NoH is 175 PgC).

• This increase is substantially larger than our corresponding estimate of 0.2 (0.1, 0.3) GtC yr−1 in 2000–2019.
• Consequently, the GCB estimate yields a larger net sink of −1.9 ± 1.0 GtC yr−1 in 2012–2021 than all the other approaches.
• The main analysis is restricted to comparison of the net cumulative LULCC flux between 1850 and 2014, but a discussion of the comparison over the full respective time periods is given in Appendix B2.
• Differences HI-REG (b, e, h) and LO-REG (c, f, i) of BLUE-LUH2 primary land area for the same years as in panels (a, d, g).
• The dataset captures the challenge of reconstructing the LULCC of the past.LUH2 is the land-use dataset that is – besides many other studies – also applied in CMIP6 (Eyring et al., 2016) for simulations with process-based DGVMs, like in LUMIP (Lawrence et al., 2016).

2 How does past uncertainty impact future scenarios?

Another difference that can influence results comparing bookkeeping models and DGVMs is that the former approach uses constant (present-day) carbon densities, while DGVMs work with variable carbon densities which respond to environmental conditions. Nevertheless, the results presented here provide a reference for comparisons with the upcoming CMIP6 model simulations. The uncertainty of agricultural area is largest at the beginning of the time series (Fig. A1b) and decreases with time.

Model description of the Bookkeeping of Land Use Emissions model (BLUE)

• Around 2000, the annual net LULCC flux is of similar magnitude to that in the early 20th century.
• Since we scale the BLUE carbon densities with the DGVM carbon density ratios, these uncertainties of DGVMs propagate to our ELUC and SLAND estimates.
• These updates reflect feedback gathered during the 2024 exposure draft period, as well as forward-looking solutions led by state CPA societies and licensing boards.
• Interestingly, the cumulative net land-use change flux over Oceania is larger in HI1700 rather than LO1700 because few transitions occur before 1700, so basically all transitions are captured in the analysis period.
• Although total harvest biomass is designed to be equal across scenarios after (Hurtt et al., 2020), this is not true for harvested area, since harvested area is derived such that the demanded harvested biomass can be fulfilled.
• In the global carbon budgets since 2017 (Friedlingstein et al., 2019; Le Quéré et al., 2018a, b), FLUC estimates forrecent decades are taken as the mean of the estimates of two BK models, theone from Houghton and Nassikas (2017) (HN2017) and the BLUE model described in Hansis et al. (2015).

Figure 1Schematic description of the BLUE model set up and of the changesmade in each of the factorial simulations (highlighted in blue boxes andsummarised in Table 2). The model is forced by a map of grid-cell-level land-use transitions occurring at time t (gross vs. net). These are thencombined with a potential vegetation map of 11 natural vegetation types(Table A1), each having specific carbon densities in vegetation and soilpools (Cdens), to calculate the carbon dislocated by each transition.

Derivation of land-use emissions and of the natural land sink

The baseline SSP5 scenario (SSP5-8.5) on the other hand starts off with a minor maximum of the net LULCC flux which is followed by a declining estimate. The initial peak in SSP5 is mainly caused by pasture expansion what are retained earnings and wood harvest (Fig. A3); the evolution of secondary land and cropland is similar to that in the SSP4 baseline, but less area is used for pasture. In the alternative 3.4OS scenario, which differs from the SSP5 baseline mainly after 2040, a secondary peak after around 2050 is present, mainly caused by crop expansion over pasture.

Bookkeeping estimates of the net land-use change flux – a sensitivity study with the CMIP6 land-use dataset

• Both BLUE and HN2017 add emissions from peat burning (van der Werf et al., 2017) and drainage (Hooijer et al., 2010) in a post-processing step.
• Increased uncertainties in crop and abandonment before 1850 are largely related to uncertainties about the magnitude of shifting cultivation and the extent of agricultural areas described in the HYDE dataset.
• Further division by LULCC activity is discussed in the following and shown in the Supplement (see Fig. S1).
• Figure 5Cumulative net LULCC flux for the period 1850–2014 from REG1700 (a) as well as the difference HI1700 – REG1700 (b) and LO1700 – REG1700 (c).
• 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).

If one scenario has continuously more LULCC than another, it will continue to produce a larger net LULCC flux, and therefore no crossing points will occur. However, if the rate of LULCC varies differently with time in two scenarios, then the simulation with an initially larger number of LULCC activities exhibits fewer transitions towards the end. More information on properties and origins of crossing points in our analysis is given in Appendix B1. Table 3Overview of future sensitivity experiments, continued from simulations with starting year 1700 for all three LULCC scenarios (see Table 2).

• Our baseline scenario (REG1700) exhibits a cumulative net LULCC flux of 242 PgC for the period 1850–2014.
• For calculating the BIM, emissions from peat fires and peat drainage from external datasets are added to ELUC and to the net land flux estimates (see Methods).
• However, observation-based maps ofvegetation and soil C densities in both disturbed and undisturbed land would be highly valuable, as they could be used in BK models to reduceuncertainties in FLUC.
• More stringent mitigation policies (RCP3.4) result in an initial plateau of the net LULCC flux up to the middle of the 21st century, followed by a peak in the second half of the 21st century of similar magnitude to the maximum in the 1950s.
• BLUE and HN2017 FLUC in 1850–2015 show better agreement in temporal variability, mostly because fact that the C density and allocation parameterisations of HN2017 dampen the effect of differences in land-use change transitions.
• The terrestrial carbon budget (Table 1) is composed of CO2 fluxes due to anthropogenic land-use changes (e.g., deforestation, afforestation) and due to environmental changes on land (effects of rising CO2 levels, climate change, and nitrogen deposition).
• The cumulative net LULCC flux in the LO scenario exceeds the values in the HI scenario in both setups, but the relative magnitude of the sensitivity (spread along the y axis for points with same x-axis base) of the cumulative net LULCC flux to LULCC and starting year of a simulation depends on the period considered.

Bookkeeping models calculate ELUC by combining the area affected by LULUCF with carbon densities of vegetation and soil and empirical growth and decay curves of carbon stored in vegetation, soil, and harvested wood products (see Methods). Key features of bookkeeping models are that they enable the separation of direct anthropogenic fluxes from natural fluxes on land and their traceability of ELUC to specific LULUCF events7,8,9,10. SLAND estimates stem from simulations of Dynamic Global Vegetation Models (DGVMs) that are conducted within the TRENDY model intercomparison project11. DGVMs are process-based carbon cycle models that simulate plant and soil processes in response to external environmental drivers, such as rising CO2 levels and meteorological and climate variability12,13. The LULCC uncertainty has a comparable impact on the cumulative net LULCC flux to including harvest and gross transitions, while its impact on most recent annual estimates is about 3 times smaller. For the starting years presented here (850, 1700 or 1850), the spread in cumulative net LULCC flux is about the same order as that from including gross transitions but can be neglected for annual fluxes in recent years.

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. The simulation outputs were compared across a variety of metrics and diagnostic tools including secondary area and mean age, global gross and net transitions, and cumulative gross and net loss of aboveground biomass. Gasser et al. (2020) use a hybrid model (the OSCAR model) combining bookkeeping properties (tracking the effect of LULCC activities) and biogeophysical properties from a DGVM to estimate uncertainties acting on annual and cumulative CO2 emissions.

Geological Net Zero and the need for disaggregated accounting for carbon sinks

Additionally, estimates of the natural land sink must reflect the impacts of LULUCF activities on environmental effects by considering the transient land cover instead of a theoretical pre-industrial state. Our framework provides the tool to quantify all relevant fluxes of the terrestrial carbon budget separately and in a spatially explicit way. This not only delivers a fully consistent terrestrial carbon budget on its own, but would also make it possible to correct the current GCB estimates by subtracting RSS from SLAND and by replacing ELUC,pd by ELUC,trans. Further, it is crucial to link reporting and certification frameworks, the development of which are currently widely underway50, Legal E-Billing directly with the scientific carbon budget approach and thus to avert double-counting of CO2 sinks or omission of sources, which may otherwise put reaching national and global climate targets at risk. The budget imbalance (BIM) is a measure of the mismatch between the estimated CO2 emissions from land-use change (ELUC) and fossil fuels (EFOS) and the estimated CO2 sinks on land (SLAND), in the ocean (SOCEAN), and in the atmosphere (GATM). The bookkeeping model BLUE uses ELUC,trans (based on transient environmental conditions) and SLAND,trans (under actual, transient land cover).

TRENDY data

The different trends of BLUE and HN2017 in the 1950s and after 1990 are instead largely attributable to the different LUC forcing (Gasser et al., 2020). Figure A1Global areas of the four BLUE land-cover types (primary land, secondary land, crop and pasture) based on the aggregated LUH2 input data (a, b) and their temporal net change (c, d). The amount of secondary and agricultural land in 850 is small compared to primary vegetation (Fig. A1a and b, less than about 1000 and 200 million ha, respectively, compared to more than 8000 million ha of primary land). From around 1700, the area of agricultural land expands more rapidly, and from around 1850, the same is true for secondary land (Fig. A1c and d, respectively). Abandonment and crop expansion (Fig. A2a) are of similar magnitude due to shifting cultivation dominating gross LULCC (not shown), especially until 1750.