Uncertainty and emergent constraints on enhanced ecosystem carbon stock by land greening

Chenyu Bian, Jianyang Xia, Xuanze Zhang, Kun Huang, Erqian Cui, Jian Zhou, Ning Wei, Ying-Ping Wang, Danica Lombardozzi, Daniel S. Goll, Jürgen Knauer, Vivek Arora, Wenping Yuan, Stephen Sitch, Pierre Friedlingstein, Yiqi Luo

Research output: Contribution to journalArticlepeer-review

Abstract

Significant land greening since the 1980s has been detected through satellite observation, forest inventory, and Earth system modeling. However, whether and to what extent global land greening enhances ecosystem carbon stock remains uncertain. Here, using 40 global models, we first detected a positive correlation between the terrestrial ecosystem carbon stock and leaf area index (LAI) over time. Then, we diagnose the source of uncertainty of simulated the sensitivities of ecosystem carbon stock to LAI based on a traceability analysis. We found that the sensitivity of gross primary productivity (GPP) to LAI is the largest contributor to the model uncertainty in more than 60% of the vegetated grids. Using the ensemble of four long-term global data sets of GPP and three satellite LAI products from 1982 to 2014, we provided an emergent constraint on the ecosystem carbon stock increase as 0.75 ± 0.46 kg C m−2 per unit LAI over global land areas. Furthermore, the biome-based results reveal that the tropical forest regions have the highest inter-model variation and model bias. Overall, this study identifies the uncertainty source and provides constrained estimates of the greening effect on ecosystem carbon stock at the global scale.

Original languageEnglish
Article numbere2022MS003397
Number of pages21
JournalJournal of Advances in Modeling Earth Systems
Volume15
Issue number5
DOIs
Publication statusPublished - May 2023

Open Access - Access Right Statement

© 2023 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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