Lower land-use emissions responsible for increased net land carbon sink during the slow warming period

SLAND,pi and SLAND,TRENDY are both based on pre-industrial land cover and, thus, their difference is due to model-intrinsic differences between BLUE and the DGVMs from the TRENDY project. Uncertainties (shaded areas in time series and whiskers in inset) indicate the minimum-to-maximum range across BLUE estimates of SLAND,trans, SLAND,pi, RSS, and LASC obtained by scaling BLUE carbon densities with individual DGVMs (dots indicate individual estimates; see Methods) and one standard deviation for TRENDY estimates. The DGVM simulations with observed transient environmental conditions used observation-based temperature, precipitation, and incoming surface radiation data at 0.5×0.5 degree spatial resolution of the Climatic Research Unit (CRU) and Japanese Reanalysis (JRA; Friedlingstein et al., 2019; Harris et al., 2014). Annual time series of global atmospheric CO2 concentrations for 1700–2018 was derived from ice core data (before 1958; Joos and Spahni, 2008) merged with National Oceanic and Atmospheric Administration (NOAA) data (from 1958 onward; Dlugokencky and Tans, 2020). While HYDE agricultural areas are used in LUH2, the main difference lies in LUH2 additionally adding wood harvest from the Global Forest Resources Assessments of the FAO and sub-grid-scale (“gross”) transitions to capture shifting cultivation in the tropics. Fluxes from deforestation, changes in land cover, land use andmanagement practices (FLUC for simplicity) contributed to approximately 14 % of anthropogenic CO2 emissions in 2009–2018.

A1 A brief description of the land carbon cycle module

This could explain distinct regional discrepancies between SLAND,TRENDY and SLAND,pi, particularly in forested regions (Supplementary Fig. 11). Third, potential errors in the LULUCF data need to be considered37,38,39, as they likely contribute to the supposed regional hotspots of emissions (Fig. 1c, Supplementary Figs. 2 and 7). For example, ref. 37 found a bias between observed biomass estimates and those simulated by BLUE in south Asia, Southeast Asia, and Equatorial Africa and attributed this bias to an overestimation of prescribed wood harvest and clearing rates in the LULUCF data. This impacts ELUC, as generally high estimates in those regions increase more (in absolute terms) when transient environmental conditions are considered. Further, ref. 37 estimated an ELUC increase by 1.4 GtC yr−1 in 2000–2019 by integrating an observation-based time series of woody vegetation carbon densities into BLUE compared to a simulation with static environmental conditions of 2000.

• Figure 6d shows how the temporal variance of Snet was partitioned into the effects of ELUC and SIntact, and their covariance term (Eq. (5) in “Methods” section).
• Solid lines, termed “observed”, show how environmental effects on carbon fluxes from land-use and (land-use induced) land cover change activities (ELUC) and on SLAND are considered in current approaches.
• For the Global Carbon Budget (GCB) we follow ref. 51 and use SLAND from TRENDY under pre-industrial land cover (corresponding to SLAND,pi) and ELUC from three bookkeeping models from GCB2022 (corresponding to ELUC,pd, see Methods for further details).
• Anatoly Shvidenko provided information on land use and forest management in European Russia.
• The uncertainty of agricultural area is largest at the beginning of the time series (Fig. A1b) and decreases with time.
• The reasons for this are the lack of a compensating emissions/removals effect (as for ELUC,trans) and that impacts of environmental changes on land areas act more homogeneously and widespread compared to LULUCF impacts (compare Figs. 1a and 2, and Figs. 1c and 3a).

Climate effects of global land cover change

OCO-2 infers atmospheric CO2 mixing ratios based on the absorption of CO2 and O2 in the atmosphere from solar radiation in the near infrared. From these measurements, the mixing ratio as a function of altitude (or pressure) is inferred using inverse methods. This is provided for our best-guess estimates https://www.bookstime.com/articles/bookkeeping-for-ebay-sellers and the two mainLULCC data sets (LUH2-GCB2019 and FRA2015) separately. Table 2Estimates of the global net land-to-atmosphere flux, LULCCemissions, land sink, and LASC. Estimates following our default (i.e.,best-guess) and alternative constraints are provided. The land sink includesthe LASC; therefore, the net land-to-atmosphere flux is strictly equal to theLULCC emissions minus the land sink.

East Asia’s Changing Urban Landscape: Measuring a Decade of Spatial Growth

The spatially explicit information provided in tables containing values of inorganic nitrogen deposition in kg km−2 was first converted to point shape files and subsequently transformed into geotiff raster files. The difference between the nitrogen deposition maps of ~2015 and ~2005 was calculated and transformed into an annual rate of nitrogen deposition change in kg ha−2 year−1. Note that the map of nitrogen deposition change, as used for the driver analysis, does not exactly cover the period from 2010 to 2019 and, thus, the effect of nitrogen deposition on ABG carbon change cannot be analyzed in its full details. We find that the land carbon sink in Eastern Europe declined over the period 2010–2019.

Land-use harmonization datasets for annual global carbon budgets

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. Methods for estimating land carbon fluxes are divided into top-down and bottom-up (see Fig. 1). Top-down atmospheric inversions rely on the analysis of atmospheric CO2 concentrations gradients to infer the accumulated effect of all CO2 sources and sinks after removing the signal of fossil CO2 emissions5.

Projecting the future of the U.S. carbon sink

• There are some important differences between the TRENDY estimates and our BLUE results.
• We saw in previous sections that this may explainpart of the large uncertainty in our LASC estimates, but it likely alsoaffects our bookkeeping LULCC emissions.
• Our framework provides the tool to quantify all relevant fluxes of the terrestrial carbon budget separately and in a spatially explicit way.
• A BLUE (Hansis et al., 2015) and H&N(Houghton and Nassikas, 2017) are bookkeeping models, whereas the others areDGVMs.b OSCAR could not be calibrated on these DGVMs due toinsufficient data.
• Typically, DGVMs tend to focus on natural ecosystems (i.e.,they have many types of forests), whereas LULCC data sets focus onanthropogenic ecosystems (i.e., more types of croplands and pastures).

Global crossing points in total net LULCC flux (Fig. 2), corresponding to larger cumulative net LULCC flux in LO than HI experiments, are thus likely due to pasture expansion and harvest. Figure A2Global gross transitions based on LUH2 baseline scenario (REG) (a) and absolute difference of high (HI) and low (LO) land-use estimates compared to the baseline LUH2 setup (b–e). For harvest (c), https://x.com/BooksTimeInc the subtransition of harvest on primary forest is shown as well. To produce the maps of net fluxes of deforestation and degradation, we used the gridded data from INPE-EM. The original resolution of the INPE-EM spatial output is 5-km and contains the aggregated emission of each grid-cell and comes in a shapefile format. This data was then converted to raster format and re-gridded to 0.5° spatial resolution by aggregating the grid-cells with the sum of fluxes.

• This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions.
• For the last decades, however, more detailed data have become available than those currently used in the models of the global carbon budgets, such as global sets of dynamic carbon-storage factors (Mason Earles et al., 2012) that define a larger number of product pools and time-varying fractions of allocation.
• This bookkeepingapproach corresponds to “definition 3” introduced by Gasserand Ciais (2013) and to “definition B” of Pongratz etal.
• In addition, we compare simulated C stocks with those of Anav et al. (2013) for the present day.
• However, we acknowledge thatOSCAR likely underestimates the HWP-related uncertainty, because there isonly one option to choose from (in the Monte Carlo setup) regarding how HWPs aresplit between pools with different decay timescales (Appendix A7).

Regional websites

The ORCHIDEE version used here has been extensively validated for northern regions41 and bookkeeping model applied globally in the recent annual GCP carbon budget update13. In this improved version, the carbon balances of intact and managed land (e.g., intact forest and recovering secondary forest) can be completely separated. This capability allows the quantification of ELUC and its individual components following Eq.