Modelled land use and land cover change emissions a spatio-temporal comparison of different approaches

Some regions with reduced emissions in the HI scenario, like Poland and south-east Asia, correspond to regions where fewer transitions of the LUH2 input data are used (Fig. A5), which is further enhanced in the HI – REG comparison. The sensitivity of the 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). Furthermore, all setups roughly exhibit the same ratio of net LULCC flux with net or gross transitions. 3 similarly shows that the cumulative net LULCC flux in the LO scenario (filled circles) exceeds the values in the HI scenario (crosses), and that REG (horizontal dash) and LO produce more similar cumulative net LULCC fluxes. The main analysis is restricted to comparison of the net cumulative LULCC flux between  1850 and 2014, but a discussion of the comparison over the full respective time periods is given in Appendix B2. Finally, feature (3) can be explained by the link between LULCC and the net bookkeeping model LULCC flux.

A2 Recalibration of the preindustrial land carbon cycle

These include large-scale processes, such as deforestation, afforestation/reforestation, and transitions between natural grassland and agricultural land, and wood harvest. For both ORCHIDEE and HN2017 estimates, the simulations were started from the year 1700, but the ELUC was examined for the period of 1850–2015. The ORCHIDEE baseline simulation was driven by variable atmospheric CO2 and CRUNCEP climate data at a 2-degree resolution (prior to 1901 climate data for 1901–1920 were recycled). Historical forest area changes and wood harvest biomass were driven by exactly the same data used in HN2017 for different geographical regions of the world (see Supplementary Figs. 1 and 2; more details are provided in Supplementary Note 1). When agricultural area changes could not be matched by changes in forest imposed by HN2017, they were implemented as transitions with natural grassland. To ensure the comparability with the HN2017 bookkeeping model, we implemented the same LUC parameterizations in ORCHIDEE.

• As discussed (Sect. 2.2.2), patterns of CO2 and climate changes may have very different effects on the fLULCC across the globe.
• Each year, for the annual Global Carbon Budget (for publication years2013–2020), the LUH dataset was extended in time for use in theparticipating DGVM and bookkeeping model simulations.
• This is further complicated because environmental changes over the historic period modified the LASC with a widely accelerated accumulation rate in later periods due to higher and faster increasing CO2 concentrations but very heterogeneously spread alterations by climatic changes.
• Taking a 20-year rotation length in the tropics33,34, accounting for shifting cultivation in ORCHIDEE (the SC-sensitivity run) yielded little new cropland being created after 1700, but generated 40% more secondary forests (Supplementary Fig. 12).
• However, it is not excludedthat future variants of the model will see implementation of such a feature.

Flexural behavior of carbon-textile-reinforced concrete I-section beams

The budget imbalance (BIM) is a measure of uncertainty in the estimated terms of the GCB, as it describes the difference between the emissions and sinks on the land, in the ocean, and in the atmosphere7. Following the GCB assessments, it is assumed that the atmospheric growth rate of CO2 (Gatm) can be measured with high confidence, whereas the assessments of the natural carbon sinks on land and in the ocean are more uncertain7. 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) https://www.instagram.com/bookstime_inc in the GCB. Annual growth rates of atmospheric CO2 show large interannual variations (IAV) that are dominated by changes in the net carbon balance of the land ecosystem1 (Snet, with a positive sign indicating a carbon sink). The temperature sensitivity of such IAV provides us with clues of the strength of future land carbon uptake in response to global warming2. Advancing our understanding of the mechanisms controlling such sensitivity, including the climate sensitivity of Snet, can help to reduce uncertainties of future projections by the coupled climate–carbon cycle models3.

Old-growth Forest carbon sink estimates

(3) Reference 16 defines errors in the order of ±0.5% (2 PgC) related to the 2000–2019 average global sum of carbon contained in woody vegetation (381 PgC). The error estimate includes pixel-level uncertainty and modeling uncertainty from parameter estimation. The global uncertainty range of ±0.5% is considered in all of our aggregated global estimates of woody biomass carbon. This means that in Table 1, the uncertainty range of ±2 PgC for the global living biomass (i.e., woody plus herbaceous vegetation) carbon stocks refers to the woody vegetation estimate only (357 PgC).

• By using biomass expansion factors, stem wood volume is converted into biomass and subsequently to carbon stocks of trees.
• Indirect anthropogenic influences (e.g., effects of increasing CO2 on plant productivity) are defined as environmental processes.
• This highlights that accounting for transient land cover and transient environmental conditions is crucial to accurately estimate SLAND, ELUC, and the net land flux and to reconcile existing approaches.
• Our results contribute to better understand the role of land management for climate mitigation.
• With the presented approach, land-use effects on SLAND can be separated from detrimental environmental impacts, both of which can put natural ecosystems under extensive stress42.

Increased uncertainties in crop and abandonment before 1850 are largely related to uncertainties about the magnitude of shifting cultivation and the extent of agricultural areas described in the HYDE dataset. Since the net flux from LULCC cannot be directly measured, we can only rely on values calculated by models, for example dynamic global vegetation models (DGVMs) https://www.bookstime.com/articles/retained-earnings-balance-sheet and bookkeeping models. 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. DGVMs, on the other hand, model the evolution of carbon pools on a process-based level and also react to climate impacts and trends.

Global carbon budget 2021

As seen for the global estimates, the approach to derive the fLULCC under transient environmental conditions introduces even more complexity, as it includes the LASC and strongly depends on the timing of LULCCs. Along these lines, the PTD undergoes a trend reversal with widely negative values in the most recent period in many regions (Figs. 6c, d and 10d), which we discuss in detail in the next section. To gain insight into the spatial trends and drivers of the three DGVM-derived fLULCC estimates and their differences, a regional analysis was conducted based on the REgional Carbon Cycle Assessment and Processes, Phase 2 (RECCAP2) regions defined in Tian et al. (2019) and shown in Fig. Since all global and regional analyses were performed based on the original model output, the RECCAP2 map was regridded to each model’s native resolution using largest area fraction remapping (to compare globally summed NBP in this study and in the GCB2019, refer to Fig. A13). Note, for grid point-wise comparison, all model output was regridded to 720×360 grid boxes using first-order conservative remapping (Jones, 1999). The large contribution of the C densities to the differences between theFLUC estimates of the two BK models found in our results highlights the importance of deriving spatially explicit maps of vegetation, and soil C densities discriminated per vegetation type would be required.