Underestimated Interannual Variability of Terrestrial Vegetation Production by Terrestrial Ecosystem Models

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  • Shangrong Lin
  • Zhongmin Hu
  • Yingping Wang
  • Xiuzhi Chen
  • Bin He
  • Zhaoliang Song
  • Shaobo Sun
  • Chaoyang Wu
  • Yi Zheng
  • Xiaosheng Xia
  • Liyang Liu
  • Tang, Jing
  • Qing Sun
  • Fortunat Joos
  • Wenping Yuan
Vegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ observations, and predictions of TEMs at a global scale, this study found that the current TEMs substantially underestimate the GPP IAV in comparison to observations at global flux towers. Our results also showed the larger underestimations of IAV in GPP at nonforest ecosystem types than forest types, especially in arid and semiarid grassland and shrubland. One cause of the underestimation is that the IAV in GPP predicted by models is strongly dependent on canopy structure, that is, leaf area index (LAI), and the models underestimate the changes of canopy physiology responding to climate change. On the other hand, the simulated interannual variations of LAI are much less than the observed. Our results highlight the importance of improving TEMs by precisely characterizing the contribution of canopy physiological changes on the IAV in GPP and of clarifying the reason for the underestimated IAV in LAI. With these efforts, it may be possible to accurately predict the IAV in GPP and the stability of the global carbon sink in the context of global climate change.
OriginalsprogEngelsk
Artikelnummere2023GB007696
TidsskriftGlobal Biogeochemical Cycles
Vol/bind37
Udgave nummer4
Antal sider15
ISSN0886-6236
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This work was supported by the Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004). Shangrong Lin was funded by the National Natural Science Foundation of China, Grant 42101319, the Fundamental Research Funds for the Central Universities, and Sun Yat‐sen University (2021qntd29). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet‐Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux‐TERN, TCOS‐Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. We also thank the PIs from global carbon budget project for providing the TRENDY v10 data.

Funding Information:
This work was supported by the Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004). Shangrong Lin was funded by the National Natural Science Foundation of China, Grant 42101319, the Fundamental Research Funds for the Central Universities, and Sun Yat-sen University (2021qntd29). This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices. We also thank the PIs from global carbon budget project for providing the TRENDY v10 data.

Publisher Copyright:
© 2023 The Authors.

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