Mapping land-use fluxes for 2001–2020 from global models to national inventories
- 1Joint Research Centre, European Commission, Ispra, Italy
- 2Ludwig-Maximilians-Universität München, Munich, Germany
- 3International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
- 4Woodwell Climate Research Center, Falmouth, MA, USA
- 5Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
- 6Global Carbon Project, CSIRO Oceans and Atmosphere, Canberra, ACT, Australia
- 7Laboratoire des Sciences du Climat et de l'Environnement CEA, CNRS, UVSQ, 91191 Gif sur Yvette, France
- 8Institute for Global Environmental Strategies, Hayama, Japan
- 9College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- 10Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, CNRS, Ecole Normale
- 11Canadian Forest Service, Natural Resources Canada, Victoria, British Columbia, Canada
- 12Basque Centre for Climate Change (BC3), Sede Building, 1, 1st floor, Scientific Campus of the University of the Basque Country, 48940, Leioa, Spain
- 13Ikerbasque, Basque Science Foundation, María Díaz Haroko Kalea, 3, 48013, Bilbo, Spain
- 14Institute of Applied Energy, Tokyo 105-0003, Japan
- 15National Center for Atmospheric Research, Boulder, CO, USA
- 16Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
- 17Max Planck Institute for Meteorology, 20146 Hamburg, Germany
- 18Max Planck Institute for Biogeochemistry, Jena, Germany
- 19NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD 20771, USA
- 20independent researcher: Celle Ligure, Italy
- 21Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
- 22School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China
- 23School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing, 210044, China
Abstract. With the focus of climate policy shifting from pledges to implementation, there is an increasing need to track progress on climate change mitigation at country level, especially for the land-use sector. Despite new tools and models offering unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between the national greenhouse gas inventories (NGHGIs) used to assess compliance with the Paris Agreement, and the Global Carbon Budget and IPCC assessment reports, both based on global bookkeeping models (BMs).
Recent evidence showed that these differences are mainly due to inconsistent definitions of anthropogenic forest CO2 fluxes. In particular, the part of the land sink that is caused by the indirect effects of human-induced environmental change (e.g., fertilization effect on vegetation growth due to increase atmospheric CO2 concentration, climate change) on managed lands is treated as non-anthropogenic by BMs, while in most cases is considered anthropogenic in NGHGIs. In addition, countries use a broader definition of managed land than BMs.
Building on previous studies, we implement an approach that adds the CO2 sink due to environmental change from countries’ managed forest area (estimated by Dynamic Global Vegetation Models, DGVMs) to the original land-use flux from BMs. This sum is expected to be conceptually more comparable to NGHGIs. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of land categories (i.e., forest land, deforestation, organic soils, other land uses) and countries.
Our results confirm a large difference in land use CO2 fluxes between the ensemble mean of the BMs, estimating a source of 4.3 GtCO2 yr-1 globally for the period 2001–2020, and NGHGIs, which estimate a sink of -1.7 GtCO2 yr-1. Most of this 6.0 GtCO2 yr-1 gap is found on forest land (3.8 GtCO2 yr-1), with differences also for deforestation (1.1 GtCO2 yr-1), other land uses (1.0 GtCO2 yr-1), and to a lesser extent for organic soils (0.1 GtCO2 yr-1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-5.1 GtCO2 yr-1) to BMs estimates, the gap between BMs and NGHGIs becomes significantly smaller both globally (residual gap: 0.9 GtCO2 yr-1) and in most regions and countries. The remaining differences mostly reflect smaller net emissions from deforestation and agricultural land in the NGHGIs of developing countries than in the BMs.
By reconciling most of the differences between NGHGIs and global models (BMs and DGVMs), offering a blueprint for operationalizing future comparisons, and identifying areas to be further investigated, this study represents an important step forward for increasing transparency and confidence in land-use CO2 flux estimates at the country level. This is crucial to support land-based mitigation investments and assess the countries’ collective progress under the Paris Agreement’s Global Stocktake.
Giacomo Grassi et al.
Status: open (until 17 Oct 2022)
Giacomo Grassi et al.
Main data from global models and countries https://zenodo.org/record/6840951#.YtGbNuxBzFw
Giacomo Grassi et al.
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