A map of the PFT distribution from HN2017 is not available, as the PFT fractions are definedon a per-country basis. When aggregated globally, the values of BLUE andHN2017 show good agreement in the global extent of croplands (15.3 and 13.8 million km2 for HN2017 and BLUE, respectively, in 2015) and forests (39.9 and 40.9 million km2 for HN2017 and BLUE, respectively, in 2015). Table 1Summary of the most important characteristics of the twoFLUC estimates from the two BK models used in the GCB2019 (BLUE and HN2017), including how FLUC is calculated in the standard version and configuration of each model, the processes represented and how they are parameterised. The model assumptions and parameterisations investigated in this study (see Table 2) are highlighted in bold.

1 Global FLUC

The actual woody biomass carbon densities from ref. 16 are assimilated in BLUE at the beginning of each year X and subsequently altered by the land-use transitions in year X. Consequently, the BLUE output of carbon stocks for year X represents the end of year X and changes in carbon stocks between year X+1 and year X are attributed to year X+1. Legacy fluxes (i.e., carbon fluxes from land-use that do not occur in the same time step as the corresponding land-use event) are tracked according to the approach explained below.

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

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. Finally, feature (3) can be explained by the link between LULCC and the bookkeeping model net LULCC flux. 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.

net into managed versus intact land

The ORCHIDEE results were rigorously validated against various observations of deforestation area, forest biomass growth, global biomass distribution, and forest carbon sinks (see “Methods” section, Supplementary Note 3). For the period of 1850–2015, the temporal magnitude and changes of ELUC derived from ORCHIDEE baseline simulation are in broad agreement with the HN2017 study, but the ORCHIDEE ELUC shows much greater IAV. This suggests that human land use modifies the response of land ecosystems to climate variability and strongly modulates the IAV of Snet. We found that managed land contributes 30–45% of the IAV of Snet, in stark contrast to only 5% when ELUC was derived by contribution margin bookkeeping models.

This was likely because the response functions for forest recovery used in https://www.bookstime.com/articles/what-is-a-performance-budget HN2017 are static and based on contemporary observations, with higher carbon stock and faster growth rates than actual historical values due to global environmental changes. In fact, the global secondary forest sink of 1.5–2 Pg C year−1 of HN2017 in 2000–2009 was higher than several other estimates. Using satellite-derived forest age distribution and the LPJ DGVM, Pugh et al.23 estimated a global secondary forest sink of only 0.53 Pg C year−1, excluding environmental change effects, much lower than HN2017. Similarly, the global secondary forest carbon sink estimated by Shevliakova et al.30 of 0.35–0.6 Pg C year−1 using the LM3V model, and the estimate by Yang et al.31 of 0.36 Pg C year−1 using the ISAM-NC model were both lower than HN2017.

• The probability of assimilation into the group decreases monotonically with deviance magnitude, but this has a non-monotonic effect on stereotype change.
• We first performed a baseline ORCHIDEE simulation to include the same LUC processes as in HN2017.
• HN2017 has higher C stocks in soil both for the pre-industrial period and the present day, compared to BLUE simulations, which are close to the values estimated by Anav et al. (2013).
• The LULCC dataset is found to cause the least uncertainty cumulatively, though the trend of the annual LULCC flux based on the two datasets has opposing signs in recent years.
• We use “traits” to refer to any features of the targets under consideration, including behavior.