The sensitivity of the net LULCC flux to uncertainties of pasture and overall uncertainty of LULCC over Oceania is relatively small. 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. 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. It should be noted that the extent of the LULCC areas in BLUE sometimes differs from the LUH2 input dataset, even for the nine main experiments, mainly because of a mismatch in PFTs between the LUH2 (harvest) input and the BLUE model.
Data availability
The largest differences to the default setup and the biggest improvements concerning the reconciliation with other datasets are found for tropical and boreal forests. 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. In other words, ~88% (2.1 PgC yr−1) of the IAV of the net carbon fluxes from woody biomass (2.4 PgC yr−1) carbon is due to environmental effects and their synergies on ELUC or conversely ~12% of the IAV (0.3 PgC yr−1) is attributable to LULCC (Table 1). The same relation between biomass carbon simulated under fixed vs. transient climate is also shown for the TRENDY simulations, although our estimates suggest a stronger contribution of environmental processes to the IAV of carbon fluxes from vegetation. Between 2001 and 2018, SLAND,B amounts to −1.6 PgC yr−1 (−1.5 PgC yr−1 for 2001–2019) based on our BLUE simulations, suggesting a ~13% smaller sink than the TRENDY multi-model average (Supplementary Table 2).
Comparison of uncertainties in land-use change fluxes from bookkeeping model parameterisation
- SLAND,trans is substantially lower (i.e., a weaker sink) than SLAND estimated with the conventional approach under pre-industrial land cover SLAND,pi, which yields a sink of −3.7 (−2.6, −5.2) GtC yr−1 in 2012–2021.
- Furthermore, we compare global and regional environmental carbon fluxes in the form of SLAND,B from our approach to estimates of an ensemble of TRENDY models for the period 2001–2018.
- 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).
- In most grid cells, the difference between SHNFull and SBL-Net is dominated by the effects of the parameterisation of C densities in gross fluxes and allocation rules for abandonment fluxes.
- Interestingly, the reduction in cumulative net LULCC flux is largest in HI850NoH if considering the whole simulation (not shown), but from 1850 (Fig. 3), LO850NoH and REG850NoH show the largest reduction by omitting wood harvest.
- Regularly reviewing and adjusting billing models is crucial to ensure they remain competitive and fair.
The estimates of annual net LULCC flux estimates in 2099 (Table 4) indicate a reduction of sensitivity to LULCC uncertainties from ±0.15 PgC yr−1 to between ±0.07 and ±0.02 PgC yr−1, respectively, for SSP4-6.0 and SSP5-3.4OS. Note that with an accuracy of the net LULCC flux of 0.1 PgC yr−1, a difference in the future scenarios due to LULCC uncertainty only remains in SSP5-8.5. The difference in net LULCC flux between historical LULCC uncertainty setups for individual scenarios in 2099 is about 50 % of their spread in 2014. In both SSP4 and SSP5, the cumulative net LULCC flux is larger bookkeeping model with lower RCP value (values in brackets in Table 4).
Description of Additional Supplementary Files
- The largest differences to the default setup and the biggest improvements concerning the reconciliation with other datasets are found for tropical and boreal forests.
- Uncertainties in FLUC estimates arise from many different sources, including differences in model structure (e.g. process based vs. bookkeeping) and model parameterisation.
- 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.
- Another case study highlights a firm that adopted value-based pricing, aligning fees with the perceived value of services delivered.
Most use double-entry accounting, which complies with generally accepted accounting principles (GAAP). The dataset was remapped to the BLUE fixed assets resolution of 0.25∘ through conservative remapping (i.e., area-weighted averaging). A major advantage of our framework is that it can be extended flexibly to updated datasets and can constantly be improved with more observational datasets being made available. 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. For easier comparison of direct model output, we do not include these post-processing steps.
Properties of land-use areas and LULCC activities are first discussed in the baseline scenario (here called REG) and then differences in the high (HI) and low (LO) LULCC scenarios are compared. Next, we want to analyse the magnitude of legacy emissions at the end of the historical simulations in 2014 and how much they are affected by past LULCC uncertainty. The magnitude of the annual net LULCC flux is determined by the size of the disequilibrium pools, which aggregate information of past LULCC events. If these disequilibrium pools are similar between two setups in a given year and the upcoming LULCC events are identical, then the annual net LULCC flux in the following years will be similar as well. The time series of all three historical uncertainty estimates (Fig. 1) shows the known feature of a peak in 1960 (Hansis et al., 2015; Friedlingstein et al., 2019). Before around 1960, the net LULCC flux is almost continuously rising and levels decrease after 1960 to the end of the historical LULCC dataset in 2014.
- Additionally, regions characterized by strong land-use disturbances in the last decades, such as many tropical forest areas in Latin America, Africa, and Southeast Asia, have started to contribute substantially to reducing sinks.
- 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.
- The higher values of ELUC,trans are related to enhanced carbon uptake in vegetation and soil in response to a rising atmospheric CO2 concentration and other potentially favorable environmental effects (e.g., nitrogen deposition; see Supplementary Fig. 2 for a map of ELUC,trans).
- In particular, the last years of the baseline LUH2 scenario have been substantially revised for subsequent analyses related to the annual GCB.
- The increasing number of transitions in the 20th century in the low land-use simulations will thus increase the difference in emissions between the two alternative scenarios.
- Of all available future scenarios, these four were selected for this study because they are based on the same two SSP scenarios but describe a range of possible RCP scenarios.
- The natural carbon sinks in terrestrial vegetation and soils provide an immense buffer for anthropogenic emissions, currently sequestering about one-third of fossil and land-use change CO2 emissions1.
DGVM estimates of the terms in the terrestrial carbon budget
Europe, Asia and Africa exhibit the largest sensitivity of cumulative net LULCC flux to LULCC uncertainties in the REG, HI and LO simulations starting in 1700 (Fig. 5). In most regions, HI1700 produces a smaller cumulative net LULCC flux than REG1700 and the cumulative flux is generally larger in LO1700 than REG1700. However, there are large coherent areas over Central and North America and northern Europe/Asia with reduced cumulative net LULCC flux in LO1700 compared to REG1700. The sensitivity of the Retail Accounting cumulative net LULCC flux to net versus gross transitions (Fig. 3, fifth column, about 13 % for REG1700) is of a similar order of magnitude as that from the starting year of a simulation (StYr).