However, for any direct comparisons with only the TRENDY DGVMs, we restrict our SLAND,B estimates to the time frame of the TRENDY data on SLAND,B (2001–2018). We use several output variables from the five different BLUE simulations to derive ELUC and SLAND. In the following, we employ the conceptual framework Grocery Store Accounting of ref. 14 to describe the carbon fluxes of each BLUE simulation. The framework distinguishes between E and L, the prefix δ, and the subscripts p, m, and n. Δ describes the effect of environmental changes on a variable (our setup captures this by the transient carbon densities), and p, m, and n indicate potential natural (p), managed (m), and natural (n) land. Potential natural land refers to the land cover as it would exist without human interventions.
2. Biennial Areas and Flux of Forest Activities
- Figure A5Differences in primary land area in BLUE and LUH2 in 2014 for REG850 (a), REG1700 (d) and REG1850 (g).
- In terms of the interannual variability (IAV) of the net carbon fluxes from global woody vegetation (Table 1), we find that the IAV is on average around eight times larger when considering environmental effects on woody biomass carbon.
- The 5 %–10 % sensitivity of the net LULCC flux to LULCC uncertainties (about 1.55 to 1.75 PgC yr−1) can mainly be explained by the uncertainty of transitions.
- The simulation with net transition (SBL-Net) reduces differences in the average and interannual variability of FLUC estimates from BLUE and HN2017.
The latter is most likely caused by pasture expansion occurring on previously less intensively used land with thus larger carbon stocks. If wood harvest is neglected, all other LULCC activities approximately produce the same spread of cumulative net LULCC flux; i.e. the ratio of a simulation with and without wood harvest is about 1 (see Table 2 for reference simulations). Note that in the experiments without harvest, the cumulative net LULCC flux from harvest is not zero because a small contribution of transitions from primary to secondary land due to rangeland expansion is counted as harvest. Due to the high computational efficiency of the bookkeeping model, several sensitivity experiments can be produced and an exhaustive comparison of common factors impacting the total net LULCC flux is possible. Here, the impact of modelling wood harvest and shifting cultivation as land management processes is compared to the impact of uncertainties of the LULCC dataset and the initialisation year of the LULCC simulation.
Factors affecting the price of accounting software
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). The analysis of the contributions from the four LULCC activities to the total net LULCC flux sensitivity reveals that (1) LULCC uncertainty from harvest causes largest sensitivity in the cumulative net LULCC flux, followed by equal contributions from abandonment and pasture and negligible sensitivity due to crop uncertainty. For harvest, the sensitivity is asymmetric; i.e. the net LULCC flux due to harvest in the HI scenario deviates further from REG than in the LO scenario. (2) Uncertainties in wood harvest cause large sensitivity to starting year of the simulation (StYr), as well as to IC and Trans in the artificial LULCC experiments. Table 2Overview of main experiments (first two rows; see also Table 1) and additional sensitivity experiments (third to fifth rows). 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).
- However, it required careful estimation of project scope and time to ensure profitability.
- Furthermore, all setups roughly exhibit the same ratio of net LULCC flux with net or gross transitions.
- For each experiment, the first number is the SSP setup and the second the prescribed RCP value.
- The estimated uncertainty of FLUC in the global carbon budgets is thus approximately 0.7 PgC yr−1 or approximately ±50 % of the average value, substantially larger than that of fossil fuel emissions.
- This is reflected in the increasing carbon densities within vegetation and soil for most biomes17,18,19 (Supplementary Figs. 3 and 4).
B1 Discussion of crossing points of net LULCC flux simulations
Since we scale the BLUE carbon densities with the DGVM carbon density ratios, these uncertainties of DGVMs propagate to our ELUC and SLAND estimates. This is explained by the fact that the scaling with DGVM carbon density ratios affects both carbon emissions and removals (Fig. 1b), which partly cancel out. The reasons for this are the lack of a compensating emissions/removals effect (as for ELUC,trans) and that impacts of environmental changes on land areas act more homogeneously and widespread compared to LULUCF bookkeeping model impacts (compare Figs. 1a and 2, and Figs. 1c and 3a). These uncertainties may be reduced in the future, as our DGVM-based carbon density dataset can easily be updated to new and improved versions of DGVMs or other model- or observation-based transient carbon density estimates.
1. Trends of Carbon Density Series
Following a transition, C stocks in the online bookkeeping different pools will decay followingresponse curves with characteristic decay times (fast for biomass pools andslow for soil pools). To estimate changes in C stocks, the models rely onvalues of C density in above- and below-ground pools which are plant functional type (PFT) specificand based on measurements (Table A2). However, the models differ in the number of plant functional types (Table A1) and their spatial distribution(per country in HN2017 and spatially explicit in BLUE).
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). The LUH2 dataset (Hurtt et al., 2020) provides historical land-use estimates from 850 with uncertainty estimates for agricultural land area (from the History Database of the Global Environment, HYDE; Klein Goldewijk et al., 2017) and wood harvest (Zon and Sparhawk, 1923; Kaplan et al., 2017). 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).
