Underestimated Interannual Variability of Terrestrial Vegetation Production by Terrestrial Ecosystem Models. Lin, S., Hu, Z., Wang, Y., Chen, X., He, B., Song, Z., Sun, S., Wu, C., Zheng, Y., Xia, X., Liu, L., Tang, J., Sun, Q., Joos, F., & Yuan, W. Global Biogeochemical Cycles, 37(4):e2023GB007696, April, 2023. ![Underestimated Interannual Variability of Terrestrial Vegetation Production by Terrestrial Ecosystem Models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract 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. , Key Points Current terrestrial ecosystem models (TEMs) substantially underestimate the interannual variability (IAV) of gross primary production (GPP) in comparison to observations at global flux sites The IAV of GPP in TEMs is strongly depended on leaf area index (LAI), which is one of the causes for the underestimation of IAV in GPP and the simulated IAV in LAI from TEMs is much less than the observation Precisely characterizing the contribution of vegetation physiological changes may improve the performance of predicting IAV in GPP from TEMs
@article{lin_underestimated_2023,
title = {Underestimated {Interannual} {Variability} of {Terrestrial} {Vegetation} {Production} by {Terrestrial} {Ecosystem} {Models}},
volume = {37},
issn = {0886-6236, 1944-9224},
url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GB007696},
doi = {10.1029/2023GB007696},
abstract = {Abstract
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.
,
Key Points
Current terrestrial ecosystem models (TEMs) substantially underestimate the interannual variability (IAV) of gross primary production (GPP) in comparison to observations at global flux sites
The IAV of GPP in TEMs is strongly depended on leaf area index (LAI), which is one of the causes for the underestimation of IAV in GPP and the simulated IAV in LAI from TEMs is much less than the observation
Precisely characterizing the contribution of vegetation physiological changes may improve the performance of predicting IAV in GPP from TEMs},
language = {en},
number = {4},
urldate = {2024-11-15},
journal = {Global Biogeochemical Cycles},
author = {Lin, Shangrong and Hu, Zhongmin and Wang, Yingping and Chen, Xiuzhi and He, Bin and Song, Zhaoliang and Sun, Shaobo and Wu, Chaoyang and Zheng, Yi and Xia, Xiaosheng and Liu, Liyang and Tang, Jing and Sun, Qing and Joos, Fortunat and Yuan, Wenping},
month = apr,
year = {2023},
pages = {e2023GB007696},
}
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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. , Key Points Current terrestrial ecosystem models (TEMs) substantially underestimate the interannual variability (IAV) of gross primary production (GPP) in comparison to observations at global flux sites The IAV of GPP in TEMs is strongly depended on leaf area index (LAI), which is one of the causes for the underestimation of IAV in GPP and the simulated IAV in LAI from TEMs is much less than the observation Precisely characterizing the contribution of vegetation physiological changes may improve the performance of predicting IAV in GPP from TEMs","language":"en","number":"4","urldate":"2024-11-15","journal":"Global Biogeochemical Cycles","author":[{"propositions":[],"lastnames":["Lin"],"firstnames":["Shangrong"],"suffixes":[]},{"propositions":[],"lastnames":["Hu"],"firstnames":["Zhongmin"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Yingping"],"suffixes":[]},{"propositions":[],"lastnames":["Chen"],"firstnames":["Xiuzhi"],"suffixes":[]},{"propositions":[],"lastnames":["He"],"firstnames":["Bin"],"suffixes":[]},{"propositions":[],"lastnames":["Song"],"firstnames":["Zhaoliang"],"suffixes":[]},{"propositions":[],"lastnames":["Sun"],"firstnames":["Shaobo"],"suffixes":[]},{"propositions":[],"lastnames":["Wu"],"firstnames":["Chaoyang"],"suffixes":[]},{"propositions":[],"lastnames":["Zheng"],"firstnames":["Yi"],"suffixes":[]},{"propositions":[],"lastnames":["Xia"],"firstnames":["Xiaosheng"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Liyang"],"suffixes":[]},{"propositions":[],"lastnames":["Tang"],"firstnames":["Jing"],"suffixes":[]},{"propositions":[],"lastnames":["Sun"],"firstnames":["Qing"],"suffixes":[]},{"propositions":[],"lastnames":["Joos"],"firstnames":["Fortunat"],"suffixes":[]},{"propositions":[],"lastnames":["Yuan"],"firstnames":["Wenping"],"suffixes":[]}],"month":"April","year":"2023","pages":"e2023GB007696","bibtex":"@article{lin_underestimated_2023,\n\ttitle = {Underestimated {Interannual} {Variability} of {Terrestrial} {Vegetation} {Production} by {Terrestrial} {Ecosystem} {Models}},\n\tvolume = {37},\n\tissn = {0886-6236, 1944-9224},\n\turl = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GB007696},\n\tdoi = {10.1029/2023GB007696},\n\tabstract = {Abstract\n 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.\n , \n Key Points\n \n \n \n Current terrestrial ecosystem models (TEMs) substantially underestimate the interannual variability (IAV) of gross primary production (GPP) in comparison to observations at global flux sites\n \n \n The IAV of GPP in TEMs is strongly depended on leaf area index (LAI), which is one of the causes for the underestimation of IAV in GPP and the simulated IAV in LAI from TEMs is much less than the observation\n \n \n Precisely characterizing the contribution of vegetation physiological changes may improve the performance of predicting IAV in GPP from TEMs},\n\tlanguage = {en},\n\tnumber = {4},\n\turldate = {2024-11-15},\n\tjournal = {Global Biogeochemical Cycles},\n\tauthor = {Lin, Shangrong and Hu, Zhongmin and Wang, Yingping and Chen, Xiuzhi and He, Bin and Song, Zhaoliang and Sun, Shaobo and Wu, Chaoyang and Zheng, Yi and Xia, Xiaosheng and Liu, Liyang and Tang, Jing and Sun, Qing and Joos, Fortunat and Yuan, Wenping},\n\tmonth = apr,\n\tyear = {2023},\n\tpages = {e2023GB007696},\n}\n\n\n\n","author_short":["Lin, S.","Hu, Z.","Wang, Y.","Chen, X.","He, B.","Song, Z.","Sun, S.","Wu, C.","Zheng, Y.","Xia, X.","Liu, L.","Tang, J.","Sun, Q.","Joos, F.","Yuan, W."],"key":"lin_underestimated_2023","id":"lin_underestimated_2023","bibbaseid":"lin-hu-wang-chen-he-song-sun-wu-etal-underestimatedinterannualvariabilityofterrestrialvegetationproductionbyterrestrialecosystemmodels-2023","role":"author","urls":{"Paper":"https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GB007696"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/tereno","dataSources":["cq3J5xX6zmBvc2TQC"],"keywords":[],"search_terms":["underestimated","interannual","variability","terrestrial","vegetation","production","terrestrial","ecosystem","models","lin","hu","wang","chen","he","song","sun","wu","zheng","xia","liu","tang","sun","joos","yuan"],"title":"Underestimated Interannual Variability of Terrestrial Vegetation Production by Terrestrial Ecosystem Models","year":2023}