The difference between these simulations and SBL-NET provides an estimate of FLUC differences each including one set of parameters from HN2017 in BLUE. For SHNFull, the difference with SBL-NET is not expected to be simply the sum of the corresponding SHNCdens, SHNAlloc andSHNt differences because of interactions between C densities,allocation fractions and response times, with differences bookkeeping model in model structureand LUC forcing, as described in Fig. Our baseline scenario (REG1700) exhibits a cumulative net LULCC flux of 242 PgC for the period 1850–2014. The sensitivity range due to LULCC uncertainty and starting year is about 22 % for comparable setups. In the nine main experiments, the cumulative net LULCC flux is at least 201 PgC (HI850) and at most 264 PgC (LO1850).
1. Land use datasets
Contrary to C densities (Sect. 4.1), at themoment no global dataset of allocation parameters exists that could becompared to the allocation fractions used here. BLUE and HN2017 FLUC in 1850–2015 show better agreement in temporal variability, mostly because fact that the C density and allocation parameterisations of HN2017 dampen the effect of differences in land-use change transitions. Europe shows 7 % higher cumulative FLUC for SBL-Net than SBL, likely because of the importance of subpixel post-abandonment recovery and re-/afforestation dynamics in Europe (Bayer et al., 2017; Fuchs et al., 2015). The BLUE simulation for GCB2019 (SBL, dark blue line) estimates higher emissions from LUC than HN2017 (black line). The cumulative emissionsbetween 1850–2015 (Fig. 2, right panel) are 139 PgC for HN2017 and 245 PgCfor SBL.
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It is important to note that the historical scenarios (HI, REG and LO) neither start nor end with the same area distribution. The transitions (Fig. A2b–e) show largest uncertainty in wood harvest, with a small contribution from wood harvest on primary land. Compared to total wood harvest, the contribution of harvest changing cover type from primary to secondary land is relatively small. Although total harvest biomass is designed to be equal across scenarios after (Hurtt et al., 2020), this is not true for harvested area, since harvested area is derived such that the demanded harvested biomass can be fulfilled. Since the other LULCC activities influence the available biomass, more or less area might be required in order to fulfil the harvested biomass demand.
1 How do LULCC uncertainties influence overall emitted carbon?
29% (BLUE minus H&N21), whereas the difference to the TRENDY multi-model average (1.4 PgC yr−1) is reduced by 88% compared to the default BLUE setup. The land-use and cover change (LUCC) is a major factor in the terrestrial carbon cycle. However, the changes of vegetation biomass and soil carbon sequestration caused by land-use conversion have large uncertainties. In the study, we determined the LUCC features through the method of performing transition matrix and transfer trajectory analysis during 2000–2018.
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- A brief description of how the LUH2 dataset is prepared for use with the BLUE model and short discussion of the properties of the LULCC dataset are provided in the Appendix (Sects. A1 and A2).
- Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014.However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable.
- Remote sensing is the most efficient way to monitor land use and land cover change over large areas (Hansen and Loveland, 2012).
- Since the budget imbalance has been approximately constant with no trend since 1959 in the GCB assessments, we conclude that the global trend of increasing SLAND is not captured accurately (Fig. 4) in the GCB.
- Scenarios with reduced radiative forcing due to increased mitigation action (RCP3.4) produce increased cumulative net LULCC fluxes over the 21st century, since fossil fuel emissions are substituted partly by energy from biofuel (Hurtt et al., 2020).
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Cumulative net LULCC flux estimates are most sensitive to harvest uncertainties, mainly over northern Europe, northern Asia and south-eastern Asia (China and north-eastern India). Components of the cumulative net LULCC flux due to uncertainty of crop expansion and abandonment follow the pattern of shifting cultivation in the tropics, which means that the sensitivity to uncertainties in abandonment and crops is balanced with the opposite sign. The largest sensitivity of the cumulative net land-use flux to LULCC using net transitions is present over Europe from abandonment and over India and south-east Asia from uncertainties in crop transitions. 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).
(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. The cumulative net LULCC flux exhibits a reduced sensitivity to LULCC uncertainty with starting year 1850 (compare vertical spread of blue markers in the LULCC column) since the input data have smaller uncertainty in more recent years (Fig. A1). At the same time, the largest estimates of the cumulative net LULCC flux comparing experiments with different StYr are produced in simulations from 1850 (second column).
- Estimating FLUC accurately in space and in time remains, however, challenging, due to multiple sources of uncertainty in the calculation of these fluxes.
- Cancelling of primary and secondary land clearing, with primary first, gave 24 % lower emissions in Hansis et al. (2015).
- 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).
- For GCB publication years 2013–2015, the LUH-GCB dataset was built off theLUH1 dataset and was identical to that dataset for the years 1500–2005.
- Figure A3Global areas of the four BLUE land-cover types based on the LUH2 dataset in four future scenarios described in the text.
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Land use and land cover conversions
Figure A1Carbon densities in vegetation (a, c) and soil (b, d) for tropical broadleaved evergreen forests for BLUE (a, b) and HN2017 (c, d) in tC ha−1. It should be noted that even though C density values are assigned on a per-country basis in HN2017, they do not differ between countries for soil C. Note that C densities are assigned to all countries, even if evergreen broadleaved forest is not present in a given country.
2. Estimates of ELUC
- Our baseline scenario (REG1700) exhibits a cumulative net LULCC flux of 242 PgC for the period 1850–2014.
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- Therefore, differences between MapBiomas and HYDE 3.3 simulations can reach up to 0.3 PgC yr−1 in some years due the higher LULCC in MapBiomas than HYDE datasets (figure 3(a)).
- Thus, accurately quantifying the impact of LUCC on the carbon budget of terrestrial ecosystems is essential for balancing regional carbon budgets and better understanding the impact of human activities on the ecological environment.
- Since the other LULCC activities influence the available biomass, more or less area might be required in order to fulfil the harvested biomass demand.
- The results demonstrated significant losses in grassland (36.22 × 106 ha), farmland (1.39 × 106 ha), and large expansion of built-up land (9.46 × 106 ha), woodland (3.59 × 106 ha).
The total value resulted by global ELUC was $136.3 × 109 US, and the value of annual was equivalent to 3.7 times the GDP of the Central African Republic in 2015 ($5.93 × 109 US yr-1). Among the 79 countries and regions considered in this study, 54 represented the upward GDP with increased emissions, and only 25 experienced GDP growth with emission reductions. These findings highlight the pivotal role of land use change in the carbon cycle and the significance of coordinated development between GDP and carbon emissions. The simulation with net transition (SBL-Net) reduces differences in the average and interannual variability of FLUC estimates from BLUE and HN2017. The contribution of gross to FLUC is smaller than previous estimates (15 %–38 %, Arneth et al., 2017; Fuchs et al., 2015; Hansis et al., 2015) and also lower than in earlier BLUE simulations that used the same rule. Cancelling of primary and secondary land clearing, with primary first, gave 24 % lower emissions in Hansis et al. (2015).
Surprising stability of recent global carbon cycling enables improved fossil fuel emission verification
The factorial simulations with only one set of parameters changed are shown in thin lines (SHNCdens in dark red,SHNt in red, SHNAlloc in yellow). The corresponding cumulative totals between 1850 and 2015 are shown in panel (b), and values relative to SBL-Net are shown by the numbers above bars. Following a transition, C stocks in the 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).
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