Regional carbon fluxes from land use and land cover change in Asia, 1980 2009
The ORCHIDEE results indicated a considerable ELUC contribution of 28% of the global IAV in Snet compared with only 5%, when ELUC was calculated with the HN2017 bookkeeping model. Conversely, intact ecosystems explained 52% of the variability of Snet in ORCHIDEE, whereas in the classical approach of bookkeeping and residual budget, SIntact accounted for nearly all of the variability of Snet. ELUC and SIntact following IPCC AR5 and ref. 14 had a positive covariance, but it is difficult to judge whether such a covariance is realistic or an artifact. If we ascribe half of this covariance to the contribution by ELUC, then managed land contributes to ~40% of the IAV of Snet. Recovering secondary forest showed a long-term increase in carbon sink since 1850, both by ORCHIDEE and HN2017 (flux Srecov in Fig. 4), which reflected its growing area (Supplementary Fig. 4). But Srecov by ORCHIDEE showed larger IAV, which contributed to the IAV of ELUC, and had a relationship to ENSO that was in the antiphase to Elegacy (Supplementary Fig. 8).
In addition, ORCHIDEE and HN2017 both used as input data annually harvested fuel wood volumes from FAOSTAT during 1961–2015, and from compilations of historical information19. The harvested fuel wood showed low IAV perhaps due to only small variations in annual economic demand. For industrial wood, in ORCHIDEE carbon release was assumed evenly distributed over a product bookkeeping model residence time (10 years and 100 years) and in HN2017 an exponential decay was assumed with the same residence times (10 years and 100 years). Both approaches approximated the slow, gradual destruction of industrial wood products and the return of their stored carbon into the atmosphere. BGross carbon emissions from deforestation only, for years between 2000 and 2005.
1. Regional emissions
Our findings and discussions regarding DGVM studies are therefore also informative for the interpretation of CMIP6 results. BLUE is a data-driven bookkeeping model (Hansis et al., 2015) used in the GCB for LULCC flux estimates (Friedlingstein et al., 2019). We choose a bookkeeping model in contrast to a DGVM because LULCC fluxes due to individual LULCC events can be traced and because of the potential to isolate the net LULCC flux independent of climate variability, among other factors (Pongratz et al., 2014). Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux, while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate.
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Common Bookkeeping Responsibilities
A positive sign of sink flux variables indicate removal of carbon from the atmosphere, while a positive sign of emission flux variables indicates release of carbon into the atmosphere. In this study, the fluxes of EFUEL, SAIR, and SOCEAN for 1959–2015 were extracted from the Global Carbon Budget 2017 released by GCP (ref. 13). Datasets and methods used for determining land use and land cover, carbon stocks, and forest regrowth. The HYDE data model version 3.0 (Goldewijk 2001) determines land use in the DGVMs, and an updated version of HYDEv3.1 is used in the Tao et al (2013) study. The Global Land Cover 2000 (GLC 2000) and the FAO Global Ecological Zone map (FAO-GEZ) provide land use and land cover for the EDGARv4.3 dataset. Initially, and until about 1800, more natural land is converted to agricultural land in the HI scenario, but then a reversal of this trend relative to LO occurs (net transitions not shown).