Historical areas subjected to shifting cultivation between forest (or grassland) and agricultural lands were extracted from the LUH2 data42 (see Supplementary Note 2 for details of shifting cultivation implementation in ORCHIDEE), while all other drivers were the same as the baseline simulation. 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. For fuel wood harvest, the aboveground woody biomass carbon was assumed being emitted into the atmosphere during the same year as harvest occurred, whereas small branches and leaves were moved to litter pool.
Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon
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). The relative change due to neglecting gross transitions https://www.bookstime.com/articles/bookkeeping-miami is similar across LULCC setups, and for REG1700net the cumulative net LULCC flux is reduced to 211 PgC. Wood harvest causes the largest sensitivity in the cumulative net LULCC flux (the flux in REG1700NoH is 175 PgC). 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.
DGVM estimates of the terms in the terrestrial carbon budget
Greenhouse gas emissions from wood harvesting and agricultural land use change, which act as sources, partially offset this gross carbon sink from agricultural abandonment. 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). The conventional picture shows managed land as a single, composite ELUC with little IAV. Sintact was derived as the residual of other budget terms and it alone absorbs most of the IAV of Snet, and drives almost completely the atmospheric CO2 growth variation (Fig. 5a). This dominance of IAV by Sintact remains unchanged in the most recent GCP global carbon budget32.
- Key features of bookkeeping models are that they enable the separation of direct anthropogenic fluxes from natural fluxes on land and their traceability of ELUC to specific LULUCF events7,8,9,10.
- In OSCAR v3.1, the parameters describing the preindustrial steady state ofthe land carbon cycle module were recalibrated on outputs of the DGVMs thattook part in the GCB2018 (Le Quéré et al., 2018a), i.e., theTRENDYv7 models.
- Our findings suggest that the rising rates of wood harvest, together with a progressive saturation of the sink from agricultural abandonment, have been the main drivers for the declining carbon sink in Eastern Europe during 2010–2019.
- 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.
- Wood harvest causes the largest sensitivity in the cumulative net LULCC flux (the flux in REG1700NoH is 175 PgC).
- BLUE is a data-driven bookkeeping model (Hansis et al., 2015) used in the GCB for LULCC flux estimates (Friedlingstein et al., 2019).
- Fire, however, does not show a significant impact on the carbon sink or even a counterintuitive one, as higher burned area seems to enhance biomass.
Comparison of four light use efficiency models for estimating terrestrial gross primary production
We apply our approach to analyse the implications of considering environmental processes on the estimated ELUC. Furthermore, we assess uncertainties of the land cover and plant functional type distribution in BLUE. Lastly, we provide observation-based SLAND estimates for woody vegetation, which are subsequently compared to DGVMs from the TRENDY project (v8)8. 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).
2.2 Relative climate- vs. CO2-induced fLULCC components
While in reality they may be substantial (e.g. increased water use efficiency due to stomatal closure under elevated CO2), it is beyond the possibilities of the available data to quantitatively assess these synergistic effects. Table 2Overview of the TRENDY v8 DGVM model output provided and used in this study and of selected processes included that are relevant for the fLULCC. Also indicated is if a plausible derivation of the environmental equilibrium difference (EED, Eq. 5 and Sect. 2.2.1) and “present-day” vs. “transient” environmental conditions difference (PTD, Eq. 6 and Sect. 2.2.1) was possible. Figure 3Regional FLUC between 1850 and 2015 from the two BK model estimates in GCB2019 (HN2017 in black and SBL for BLUE in dark blue), the BLUE simulations with net LUC transitions and standard parameterisation (light blue, SBL-Net) and using HN2017 parameterisations (cyan, SHNFull). 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). To address the potential effect of CO2 fertilization, we acquired monthly gridded data of the atmospheric CO2 concentration as column-mean molar fraction from CAMS global greenhouse gas reanalysis (EGG4)83, which covers the period of 2003–2021.
Effects of land use/cover change on carbon storage between 2000 and 2040 in the Yellow River Basin, China
It then becomes even more pronounced ∼1950 in Brazil, equatorial Africa and China, with the latter two and Southeast Asia showing a particular strong increase after 2000 (Figs. 6a, b, 10b and A4). Overall, the https://www.facebook.com/BooksTimeInc/ LASC accumulated to more than 4 PgC in the USA, Brazil, equatorial Africa, and Southeast Asia, and to 2–4 PgC in China, Russia,southwest South and Central America, southern Africa, and South Asia (Figs. 10a and A10). As stated above, these high cumulative and annual LASC estimates mainly result from an initial high forest coverage and subsequent C losses in particular on areas where higher C stocks resulted from environmental changes over time (Sect. 3.4 and Fig. 8). Ratios of cTot were derived based on the annual averages in the last decade of the simulation period across all models (2009–2018).
Description of Additional Supplementary Files
- For harvest (c), the subtransition of harvest on primary forest is shown as well.
- H&N is thesecond bookkeeping model of the GCB2019, and this time both models aredriven by the FRA2015 data set.
- Omitting harvest causes the least reduction in HI and the most in REG, which can be explained by the relative amount of deforestation on forested primary land.
- By definition, fLULCC estimates are not directly comparable between these two different model types.
Strong reductions in RMSDHN-BLUE for SHNFull are found in BRA, RUS, CHN and SAS (top panel in Fig. 4b), explained by RMSDHN-BLUE reductions by changing the C densities in vegetation and soil pools (SHNCdens) and allocation fractions. In SEAS, cumulative FLUC is reduced when using HN2017 parameters (SHNFull) but with a higher RMSDHN-BLUE. In this region, C density parameters contribute the most to the reduction of bias, compared to SBL-Net, and both C density parameters and allocation fractions contribute to the increase in RMSDHN-BLUE.
- C Spatial distribution of the environmental contribution to ELUC averaged over 2012–2021.
- 3c and d display the same source ofuncertainty but among different versions of each of the two main data sets.This variation, which is caused by updating the data sets, is visible, for instance,when comparing older versions of the GCB with one another.
- The fLULCC_pd (and consequently the EED and PTD) could not be derived for CLM5.0, JULES, LPJ, and OCN (no S5 and S6 simulation; eight models).
- In contrast, the GCB estimate of the net land flux conceptually differs as it is the sum of the bookkeeping ELUC (resembling ELUC,pd) and the TRENDY SLAND (resembling SLAND,pi).
- ORCHIDEE-CNP and SDGVM estimates were not shown since no data from the GCB2019 were available.
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. Additionally, BLUE shows an increase in FLUC in CHN for the 2010s, while HN2017 estimates a sink due to afforestation. In all of these regions, adjusting BLUE partly or fully to HN2017 parameters does not obviously bring trends closer together, because a lowering of the 2000s FLUC in BLUE, which results from several of the factorial experiments, would lead to lower FLUC in earlier time periods as well. In order to attribute differences in FLUC between the two models to specific aspects from Table 1, we perform a set of factorial simulationswith BLUE (see Table 2), in which we replace the BLUE parameters with thosefrom HN2017 (see also schematic in Fig. 1). We then compare these simulations with the fluxes estimated by HN2017, published in Houghton and Nassikas (2017) and Friedlingstein et al. (2019). We used gridded monthly precipitation from the TerraClimate data of monthly climate and climatic water balance for global terrestrial surfaces from 1958–2019 (~4-km spatial resolution)80.
J. Land Use Sci.
If not specified otherwise, simulations are conducted with all three starting years (850, 1700 and 1850) and simulated for HI, REG and LO. The two setups with changes to initial conditions (IC) and transitions (Trans) modify the LUH2 dataset and are artificial. 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. Figure 3Smoothed global annual means (a–c) and cumulative sums(d–f) of fLULCC_trans (a, d), fLULCC_pi (b, e), bookkeeping model and fLULCC_pd (c, f) for the investigated DGVMs from 1800 to 2018. For comparison, we also included the GCB2019 bookkeeping mean (same values in all panels).
