The uncertainty of agricultural area is largest at the beginning of the time series (Fig. A1b) and decreases with time. In 850, the uncertainty around the baseline scenario is about 50 % for pasture and crop area, of which 1 % remain in 2014 (Fig. A1b). This initial uncertainty of secondary land is due to division of rangelands into secondary land and pasture for BLUE and is accounted to rangelands in the LUH2 data. Thus, the same total uncertainty is present in the LUH2 dataset and the data prepared for BLUE.
Description of Additional Supplementary Files
Case studies on fee structures in accounting practice reveal diverse billing models tailored to meet the unique needs of clients. These models include hourly billing, fixed fees, and value-based pricing, each offering distinct advantages and challenges. The selection of a billing model often depends on the complexity of services provided and client preferences. In the realm of accounting practice, billing models are crucial for defining how professional financial services are charged. One common model is the hourly billing model, where clients are billed based on the actual time spent on their projects. This model provides transparency and can be advantageous for tasks that require variable amounts of time.
- As the number and definition of PFTs is not standardized in DGVMs, not all PFTs can be directly matched to the BLUE PFTs.
- The observed woody biomass carbon densities by ref. 16 are assimilated in BLUE in several steps.
- Consequently, the IAV of the TRENDY estimates also includes dynamics of non-woody vegetation.
- The difference between SLAND,trans and SLAND,pi are the replaced sinks and sources (RSS), i.e., the lost (or gained) sinks due to degradation (or restoration) of ecosystems by land-use changes.
- Even though SHNt results in a small positive difference in cumulative FLUC relative to SBL-NET (221 PgC), effect of response-curve times is multiplicative (Fig. 1), therefore the FLUC trends are amplified (Fig. 2, left panel).
- We note that our improvements to the terrestrial carbon budget do not bring the budget imbalance to zero – this cannot be expected due to many (potentially compensating) errors that accumulate in the imbalance term as a consequence of uncertainties in each of the five budget terms.
Data availability
- 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.
- While beneficial, it necessitated transparent communication to justify the premium fees to clients.
- If several reference experiments are given, the ordering is the same as in the column header.
- 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.
- 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).
- Parameters in BLUE and HN2017 are defined on a PFT basis, but HN2017 distinguishes 20 PFTs (3 of them desert PFTs), while BLUE distinguishes 11 PFTs.
Value-based billing is a more contemporary approach, where fees are determined by the perceived value of the services provided rather than the time spent. This model aligns the accountant’s incentives with the client’s success, fostering a more collaborative relationship. However, it requires a clear understanding of the client’s goals and the accountant’s ability to deliver measurable value. As the unsteady flow simulation runs, information pertaining to the progress of the run, how many iterations the 1D and 2D components are using to solve a particular time step, and numerical stability messages will be written to the Computational Messages window. If any 1D or 2D element (cross section, storage area, or 2D cell) is not solved to within the pre-defined numerical tolerance during a time step, a message will be written to the Computational Messages window.
A consistent budgeting of terrestrial carbon fluxes
The net land flux estimates include emissions from peat fires and peat drainage from external datasets (see Methods). Uncertainties (shaded areas in time series and whiskers in inset) indicate the minimum-to-maximum range across BLUE estimates obtained by scaling BLUE carbon densities with individual DGVMs (dots indicate individual estimates; see Methods) and one standard deviation for the TRENDY and GCB2022 estimates. Our BLUE estimates are based on simulations using averaged DGVM carbon densities (see Methods); thus, they do not correspond to the mean of the BLUE estimates based on individual DGVMs. Here, we propose an approach that overcomes the mentioned limitations of BKMs and satellite-based estimates by allowing us to decompose observationally constrained estimates of carbon stocks into anthropogenic (ELUC) and environmental (SLAND) contributions. We only count fluxes resulting from gross vs net direct anthropogenic activities in the form of LULCC towards anthropogenic processes.
Over the period 1850–2014, the cumulative LULCC flux as determined by GCB2019 (Friedlingstein et al., 2019) is 195±60 PgC, compared to bookkeeping model 400±20 PgC from fossil fuels. Around the baseline estimate of 1.7 PgC yr−1 (REG1700), LULCC adds asymmetrically about ±0.15 PgC yr−1 and without harvest or gross transitions the net LULCC flux in 2014 is reduced by 0.6 PgC yr−1. The importance of LULCC uncertainty for net LULCC flux decreases with time and therefore is more relevant for the cumulative net LULCC flux than for the annual value in 2014.
Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr−1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr−1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake.
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3. Spatiotemporal Pattern of Carbon Flux
Uncertainties in FLUC estimates arise from many different sources, including differences in model structure (e.g. process based vs. bookkeeping) and model parameterisation. Quantifying the uncertainties from each source requires controlled simulations to separate their effects. Currently, the GCB uses two different types of models to provide historical estimates of anthropogenic CO2 emissions from LULUCF (ELUC) and of the natural land sink (SLAND)1,5. Bookkeeping models calculate ELUC by combining the area affected by LULUCF with carbon densities of vegetation and soil and empirical growth and decay curves of carbon stored in vegetation, soil, and harvested wood products (see Methods). 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.
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