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 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. Especially for StYr, the range is larger, resulting in a larger sensitivity of StYr compared to the LULCC uncertainty. Larger fluxes occur in runs from the year 850 if the total cumulative net LULCC flux is considered, simply because of the longer model virtual accountant simulation (see Fig. 2).
- Transitions on primary non-forested land are mainly neglected in Europe, especially Poland, the Middle East, India and western Africa (not shown).
- (4), thus including the hypothetical land sink, which, in reality, is lost due to historical ecosystem degradation (RSS).
- For the LUH2 dataset, HYDE data are interpolated and combined with annual wood harvest data from the Food and Agriculture Organization (FAO) to provide annual states and transitions.
- ELUC,pi excludes all environmental effects, ELUC,pd includes environmental effects on ELUC based on present-day environmental conditions, and ELUC,trans includes transient environmental effects on ELUC.
Effect of environmental changes on emissions from land-use change
At the same time, primary land area is reduced to about 74 % of its original extent and secondary land area is steadily increasing at a total of about 22 %. The alternative scenario (SSP5-3.4OS) is an overshoot scenario which mainly differs from the baseline scenario after 2040. The cropland area is increased by 50 % from 2010 to 2100, mainly by cultivating cropland which was previously used as pasture. By neglecting information on some of the LULCC activities from the input dataset, simulations without wood harvest and with net instead of gross transitions can be produced (see Table 2). Note that the net LULCC flux is an aggregate of all sources and sinks due to LULCC in 1 year and is not linked to net transitions; i.e. net and gross land-use transitions must not be mixed up with the net or gross LULCC flux. (1) The DGVM PFTs are translated to the BLUE PFTs (different types of forests, shrubland, grasses, and tundra) for the BLUE land-cover types primary land, secondary land, pasture, and cropland (see Supplementary Data 1 and 2).
Model description of the Bookkeeping of Land Use Emissions model (BLUE)
In 850, the uncertainty around the baseline scenario is about 50 % for pasture and crop area, of which 1 % remain in 2014 (Fig. A1b). This initial uncertainty of secondary land is due to division of rangelands into secondary land and pasture for BLUE and is accounted to rangelands in the LUH2 data. Thus, the same total uncertainty is present in the LUH2 dataset and the data prepared for BLUE.
Exposure draft: Proposed Uniform Accountancy Act Changes
- The model is forced by a map of grid-cell-level land-use transitions occurring at time t (gross vs. net).
- However, these changes would also bring down FLUC estimates in many regions that were not deemed too high in FLUC based on the constraint by observations.
- A reduction of the cumulative net LULCC flux in the IC and Trans experiments initialised in 1700 or 1850 is both due to reduced contribution from harvest and pasture (only IC) and the opposite ordering of LULCC experiment in crop and abandonment contributions.
- The two bookkeepingmodels used in the global carbon budgets may differ in their FLUC estimates due to differences in the forcing data and differences in model structure, parameterisation and in how certain processes are represented.
- The model assumptions and parameterisations investigated in this study (see Table 2) are highlighted in bold.
- The global differences between simulations result from interactions between the different factors and in the types of LUC occurring in a given point inspace and time.
To find a reference simulation, the row and column of the last table section can be combined to give one experiment setup (note that LULCC and StYr do not modify the setup, but IC, Trans, net and NoH do). If several reference experiments are given, the ordering is the same as in the column header. All three simulations include carbon stock changes due to LULUCF (L) but vary in their consideration of environmental effects on ELUC (δL). ELUC,pi excludes all environmental effects, ELUC,pd includes environmental effects on ELUC based on present-day environmental conditions, and ELUC,trans includes transient environmental effects on ELUC. The rules for allocation of displaced carbon to different pools have thestrongest effect on average FLUC, as well as their variability, followed by C density parameters.
Implementation of transient environmental forcing into BLUE
The gridded fields of the BLUE simulations can be provided by the contact author upon request. The global differences between simulations result from interactions between the different factors and in the types of LUC occurring in a given point inspace and time. Figure A4Differences in global total agricultural area in BLUE, also including results from initial condition (IC) and transition (Trans) sensitivity experiments (see Table 2). Some regions with reduced emissions in the HI scenario, like Poland and south-east Asia, correspond to regions where fewer transitions of the LUH2 input data are used (Fig. A5), which is further enhanced in the HI – REG comparison.
The sensitivity of the net LULCC flux to the uncertainty from pasture expansion (Fig. 4d) is larger from transitions (Trans, fourth column) than from initial conditions (IC, third column). A reduction of the cumulative net LULCC flux in the IC and Trans experiments initialised in 1700 or 1850 is both due to reduced contribution from harvest and pasture (only IC) and the opposite ordering of LULCC experiment in crop and abandonment contributions. 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.
Derivation of land-use emissions and of the natural land sink
However, these changes would also bring down FLUC estimates in many regions that were not deemed too high in FLUC retained earnings based on the constraint by observations. This suggests that neither the BLUE nor HN2017 setup and parameterisation can be judged as being superior to the other for all regions of the world and all time periods. The factorial analysis sheds light on the underlying reasons of the diverging trends in the 2000s, where BLUE showed an upward trend, opposing the downward trend in FLUC from HN2017.
3 Effects on gross FLUC component fluxes
- The baseline SSP5 scenario (SSP5-8.5) captures conditions of high levels of fossil fuel use, increasing global food demand and therefore increasing cropland area (about 20 % increase from 2010 to 2100, Fig. A3).
- (2020) pointed out that FLUC estimated by DGVMs and BLUE in BRA, SEAS, EU and EQAF were probably too high.
- 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.
- 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.
- The American Institute of CPAs (AICPA) and National Association of State Boards of Accountancy (NASBA) have proposed new changes to the profession’s model law.
It should be noted that the LUH2 dataset, as proposed by CMIP6, does not capture the full range of uncertainty but is an estimate based on the available data (Klein Goldewijk et al., 2017; Hurtt et al., 2020). 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). In particular, the last years of the baseline LUH2 scenario have been substantially revised for subsequent analyses related to the annual GCB. This includes updates in the underlying agricultural data from the FAO but also revisions of regionally inconsistent data (e.g. erroneous data in Brazil in the GCB 2018 results, Le Quéré et al., 2018; Bastos et al., 2020). With the current TRENDY simulation setup, it is however not possible to separate the transient effects on ELUC (i.e., δL) from the other transient effects (i.e., δEm, δEp). The global and regional fluxes from HN2017 and the BLUE simulations are provided in the Supplement.
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