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

Accrual accounting is based on the matching principle, which is intended to match the timing of revenue and expense recognition. By matching revenues with expenses, the accrual method gives a more accurate picture of a company’s true financial condition. If a business generates more than $25 million in average annual gross receipts for the preceding three years, however, it must use the accrual method, according to IRS rules.

Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterisation

If the IRS ever conducts an audit on a company, it looks at a company’s accounting records and methods. Furthermore, the IRS requires taxpayers to choose an accounting method that accurately reflects their income and to be consistent in their choice of accounting method from year to year. A LUH data not used while new LUH2 dataset was under development; b first year LUH used for BLUE, bookkeeping model as well as for DGVMs; c the specific version of HYDE 3.2 that was used for LUH2 is the August 2016 beta release. For GCB 2016, an updated LUH dataset was not provided due to ongoing work onthe creation of the LUH2 dataset. Instead, GCB used only those DGVMs thatwere based directly on HYDE, not LUH (Le Quéré et al., 2016).

Data Availability Statement

In this study, we used a recently improved version of the ORCHIDEE DGVM, which is able to separate managed versus intact land at a sub-grid scale, to investigate the role of land use in modulating the IAV of Snet. To highlight the difference of this improved DGVM and the bookkeeping approach in estimating ELUC and its contribution to the IAV of Snet, we implemented in ORCHIDEE the same LUC parameterization and forcing as one widely used bookkeeping model of Houghton and Nassikas19 (HN2017, see “Methods” section, Supplementary Note 1). For a baseline simulation to be consistent with the HN2017 study, we included only large-scale net LUC processes of deforestation, afforestation/reforestation, and transitions between natural grasslands and agricultural land, and wood harvest. We then further included local-scale shifting cultivation in a sensitivity simulation to explore the uncertainty of our results (see “Methods” section, Supplementary Note 2). 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).

Contributions of ecological restoration policies to China’s land carbon balance

Over the period 1850–2014, the cumulative LULCC flux as determined by GCB2019 (Friedlingstein et al., 2019) is 195±60 PgC, compared retained earnings to 400±20 PgC from fossil fuels. Around the baseline estimate of 1.7 PgC yr−1 (REG1700), LULCC adds asymmetrically about ±0.15 PgC yr−1 and without harvest or gross transitions the net LULCC flux in 2014 is reduced by 0.6 PgC yr−1. The importance of LULCC uncertainty for net LULCC flux decreases with time and therefore is more relevant for the cumulative net LULCC flux than for the annual value in 2014. Our study thus provides an extension to previous studies comparing sensitivities across a different set of factors by also disentangling the relevance of the initial land-cover distribution compared to the uncertainties in LULCC activities on the net LULCC flux. In addition, it updates the sensitivities of, e.g. wood harvest and shifting cultivation based on a more recent LULCC dataset, which is also the basis for CMIP6, using one bookkeeping model.

• 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.
• Bookkeeping models (Houghton, 2003; Houghton and Nassikas, 2017; Hansis et al., 2015) combine observation-based carbon densities with LULCC estimates to determine the net LULCC flux.
• For both industrial and fuel wood harvest, we started from intact forests and then move to younger cohorts in order to fulfill the prescribed annual-harvested wood biomass in the forcing data.
• The focus in Gasser et al. (2020) is on the relative importance of biogeophysical parameters, the LULCC dataset – either the LUH2 or the FRA (Forest Resources Assessment, FAO, 2015) dataset – and the inclusion of the LASC (loss of additional sink capacity, e.g. Pongratz et al., 2014) to the net LULCC flux.

Quantifying the impacts of land cover change on gross primary productivity globally

• The model is forced by a map of grid-cell-level land-use transitions occurring at time t (gross vs. net).
• Figure 6d shows how the temporal variance of Snet was partitioned into the effects of ELUC and SIntact, and their covariance term (Eq. (5) in “Methods” section).
• Thoseland-use transitions in the early 2000s, when the main correction to theBrazilian land-use data was applied, are likely to provide the biggest changesto the global carbon budget.
• The management status of forest related to either disturbance history or recovery status.
• 3, the y axis is not scaled by a reference simulation but presents the total net emissions in 2014.

LULCC differences still modulate annual net LULCC flux estimates throughout the 20th century (Fig. S2), and the largest variability of net LULCC flux, about ±0.1 to 0.3 PgC yr−1, is due to uncertainties in harvest and abandonment. In 2014, the largest impact of the remaining differences is due to harvest (about ±0.05–0.1 PgC yr−1). Figure 6d shows how the temporal variance of Snet was partitioned into the effects of ELUC and SIntact, and their covariance term (Eq. (5) in “Methods” section). The ORCHIDEE results indicated a considerable ELUC contribution of 28% of the global IAV in Snet compared with only 5%, when ELUC was calculated with the HN2017 bookkeeping model.

• The impact of the initial uncertainty is thus further reduced relative to the magnitude of the net LULCC flux in 2099, if followed by a larger cumulative net LULCC flux.
• Subsequent to generating the LUH2-GCB2019 dataset, we also recentlygenerated the LUH2-GCB2020 dataset for use in GCB2020 simulations(Friedlingstein et al., 2020).
• Despite the differences between the HYDE3.2-Aug2016 used for LUH2 v2h andthe HYDE-GCB2019 for each year up to and including 2012, the HYDE-GCB2019was closely matched to the HYDE3.2-Aug2016 dataset, with the exception ofgrid cells in the corrected regions of Brazil.
• Integrating different components of ELUC along with their IAVs into the global carbon cycle yielded a different look for the global carbon budget than conventionally seen in IPCC AR5 and, until recently, in the annual carbon budgets released by the GCP (ref. 14; Fig. 5).
• Such age thresholds were consistent with the reported secondary forest ages that reached a similar status as intact forests from field investigations45.
• Our following analysis focuses mainly on the time period of 1959–2015, during which different components of global carbon budget can be relatively well constrained owing to reliable measurements of annual atmospheric CO2 growth rate13.

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• If not specified otherwise, simulations are conducted with all three starting years (850, 1700 and 1850) and simulated for HI, REG and LO.
• The LUH2 dataset uses agricultural data from the uncertainty range A of the HYDE product, an uncertainty range based on literature and expert judgement.
• 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).
• Without accurate accounting, a business would not know where it stands financially, most likely resulting in its demise.
• 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.

Our analysis shows thatFLUC estimates for these regions, except EU would be lower if the setup of HN2017 were used, i.e. starting in 1700 instead of 850 and using net transitions, and all four regions would show even larger reductions in FLUC if the parameterisation of HN2017 were used in BLUE. 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. This suggests that neither the BLUE nor HN2017 setup and parameterisation can be judged as being Bookstime superior to the other for all regions of the world and all time periods.