Key points are not available for this paper at this time.
Abstract The terrestrial biosphere plays a major role in the global carbon cycle, and there is a recognized need for regularly updated estimates of land‐atmosphere exchange at regional and global scales. An international ensemble of Dynamic Global Vegetation Models (DGVMs), known as the “Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide” (TRENDY) project, quantifies land biophysical exchange processes and biogeochemistry cycles in support of the annual Global Carbon Budget assessments and the REgional Carbon Cycle Assessment and Processes, phase 2 project. DGVMs use a common protocol and set of driving data sets. A set of factorial simulations allows attribution of spatio‐temporal changes in land surface processes to three primary global change drivers: changes in atmospheric CO 2 , climate change and variability, and Land Use and Land Cover Changes (LULCC). Here, we describe the TRENDY project, benchmark DGVM performance using remote‐sensing and other observational data, and present results for the contemporary period. Simulation results show a large global carbon sink in natural vegetation over 2012–2021, attributed to the CO 2 fertilization effect (3.8 ± 0.8 PgC/yr) and climate (−0.58 ± 0.54 PgC/yr). Forests and semi‐arid ecosystems contribute approximately equally to the mean and trend in the natural land sink, and semi‐arid ecosystems continue to dominate interannual variability. The natural sink is offset by net emissions from LULCC (−1.6 ± 0.5 PgC/yr), with a net land sink of 1.7 ± 0.6 PgC/yr. Despite the largest gross fluxes being in the tropics, the largest net land‐atmosphere exchange is simulated in the extratropical regions.
Building similarity graph...
Analyzing shared references across papers
Loading...
Stephen Sitch
Michael O’Sullivan
Eddy Robertson
Global Biogeochemical Cycles
Centre National de la Recherche Scientifique
University of Illinois Urbana-Champaign
University of Maryland, College Park
Building similarity graph...
Analyzing shared references across papers
Loading...
Sitch et al. (Mon,) studied this question.
www.synapsesocial.com/papers/68e61c93b6db6435875aebba — DOI: https://doi.org/10.1029/2024gb008102