Terrestrial CO2 fluxes from land use, land use change, and forestry (LULUCF) accounted for about 12% of total anthropogenic CO2 emissions in the last 20 years, while land simultaneously provided a natural sink for about 29% of all CO2 emissions (Friedlingstein et al., 2021). Comparisons of anthropogenic LULUCF emissions in global models and in country reports to the United Nation Framework Convention on Climate Change (UNFCCC) revealed a substantial gap between both estimates, globally amounting to about 5.5 Gt CO2/year (Grassi et al., 2021). This gap was mainly attributed to discrepancies in areas considered as managed land in models and country reports, and to the (partial) inclusion of natural CO2 fluxes (e.g. from carbon removals by CO2 fertilization, forest fires, insect outbreaks) on managed land in many of the country reports (Grassi et al., 2021).
Building on this proposed explanation, we provide a disaggregation of country-reported CO2 emissions from LULUCF into contributions from anthropogenic and natural CO2 fluxes on country level, considering eight countries with high emissions from LULUCF. We focus on natural fluxes in managed forests since the majority of natural CO2 removals occurs in forested areas. For each country we use process-based models to estimate the natural CO2 fluxes on managed forests (which we identify through a mask of non-intact forest due to lack of information on the spatial distribution of managed forests in the country reports) and add them to the model-based LULUCF emission estimates. This approach is in line with the methodologies used by almost all investigated countries to estimate CO2 fluxes from LULUCF, which imply that natural fluxes on managed land are included in their CO2 flux estimates.
In the majority of the eight countries investigated, including natural fluxes on managed forests substantially reduces the gap between model estimates and country reports of LULUCF emissions, highlighting that the methodology suggested by Grassi et al. (2021) provides a feasible approach to make estimates more compatible also at the country level. Countries include about half of the domestic natural CO2 fluxes in their LULUCF emission reports, which shifts the reported CO2 fluxes downward, i.e., towards lower emissions or, accordingly, towards larger sinks. Large gaps present in Russia and the USA can be closed almost completely by adding natural fluxes on managed forests to model-based LULUCF emissions, revealing that the CO2 sinks from LULUCF reported by these countries are predominantly due to natural fluxes on managed forests. Also in the EU, Indonesia and China, which is the country that has the largest gap and reports the largest CO2 sink of the investigated countries, the gap is considerably reduced by including natural CO2 fluxes on managed forests. These results highlight that the methodological discrepancies between country reports and model estimates of LULUCF emissions are primarily due to accounting definitions and need to be reconsidered in a proper assessment of the country contributions to the global climate mitigation targets, as planned in the Global Stocktake in 2023.
While the presented approach provides an important step forward in bringing together model estimates and country reports, it does not achieve a complete closing of the gap. For some countries, estimates from models and country reports still differ substantially, such as for China, where differences might be due to overoptimistic estimates of the actual effects of afforestation on CO2 fluxes in the country report, underestimations of the afforested area in the input datasets used by models to calculate LULULCF fluxes, and/or limitations in the capability of models to fully integrate the large-scale afforestation in China. There are several potential reasons for the remaining gaps, including incomplete reporting by countries, uncertainties in historical land use dynamics, and model limitations. Moreover, most countries report the areas considered as managed without explicit information on their location, which prevents a precise spatial identification necessary for correctly quantifying natural fluxes on managed forests in models. For some of these factors, remote-sensing products might provide independent and spatially explicit estimates through satellite-derived classifications of land use and land cover change, quantifications of changes in biomass, or identification of managed forest areas. Additionally, the near real-time availability of satellite data might be useful for providing a temporal extension of country reports, which are usually published with a lag of several years. Remote-sensing products might thus constitute an additional, strong pillar in establishing a sound and viable methodology for a translation between model estimates and country reports of anthropogenic CO2 emissions from LULUCF.
Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D. C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., . . . Zeng, J. (2021). Global Carbon Budget 2021. Earth Syst. Sci. Data Discuss., 2021, 1-191. https://doi.org/10.5194/essd-2021-386
Grassi, G., Stehfest, E., Rogelj, J., van Vuuren, D., Cescatti, A., House, J., Nabuurs, G.-J., Rossi, S., Alkama, R., Viñas, R. A., Calvin, K., Ceccherini, G., Federici, S., Fujimori, S., Gusti, M., Hasegawa, T., Havlik, P., Humpenöder, F., Korosuo, A., . . . Popp, A. (2021). Critical adjustment of land mitigation pathways for assessing countries’ climate progress. Nature Climate Change, 11(5), 425-434. https://doi.org/10.1038/s41558-021-01033-6