TY - JOUR
T1 - Uncertainty and Emergent Constraints on Enhanced Ecosystem Carbon Stock by Land Greening
AU - Bian, Chenyu
AU - Xia, Jianyang
AU - Zhang, Xuanze
AU - Huang, Kun
AU - Cui, Erqian
AU - Zhou, Jian
AU - Wei, Ning
AU - Wang, Ying Ping
AU - Lombardozzi, Danica
AU - Goll, Daniel S.
AU - Knauer, Jürgen
AU - Arora, Vivek
AU - Yuan, Wenping
AU - Sitch, Stephen
AU - Friedlingstein, Pierre
AU - Luo, Yiqi
N1 - Funding Information:
This work was financially supported by the National Key R&D Program of China (2022YFF0802104) and Shanghai Pilot Program for Basic Research (TQ20220102). We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP5, CMIP6, and ESGF. This work used eddy covariance data acquired by the FLUXNET community, particularly by the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet‐Canada, GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS‐Siberia, and USCCC. We Thank the TRENDY team for the provision of the DGVM simulations, and the researchers of the Global Carbon Project for making their data publicly available. We Thank J. Nabel, S. Zaehle for valuable suggestions on interpreting the results.
Funding Information:
This work was financially supported by the National Key R&D Program of China (2022YFF0802104) and Shanghai Pilot Program for Basic Research (TQ20220102). We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP5, CMIP6, and ESGF. This work used eddy covariance data acquired by the FLUXNET community, particularly by the following networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, and USCCC. We Thank the TRENDY team for the provision of the DGVM simulations, and the researchers of the Global Carbon Project for making their data publicly available. We Thank J. Nabel, S. Zaehle for valuable suggestions on interpreting the results.
Publisher Copyright:
© 2023 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.
PY - 2023/5
Y1 - 2023/5
N2 - 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.
AB - 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.
KW - ecosystem carbon stock
KW - emergent constraints
KW - global land greening
KW - gross primary production
KW - traceability analysis
KW - uncertainty source
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U2 - 10.1029/2022MS003397
DO - 10.1029/2022MS003397
M3 - Article
AN - SCOPUS:85160407541
SN - 1942-2466
VL - 15
JO - Journal of Advances in Modeling Earth Systems
JF - Journal of Advances in Modeling Earth Systems
IS - 5
M1 - e2022MS003397
ER -