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Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation
Ge, Rong1,2; He, Honglin1,3; Ren, Xiaoli1; Zhang, Li1,3; Yu, Guirui1,3; Smallman, T. Luke4; Zhou, Tao5; Yu, Shi-Yong6; Luo, Yiqi7,8; Xie, Zongqiang9; Wang, Silong10; Wang, Huimin1; Zhou, Guoyi11; Zhang, Qibin9; Wang, Anzhi10; Fan, Zexin12; Zhang, Yiping12; Shen, Weijun11; Yin, Huajun13; Lin, Luxiang12
2019-03-01
Source PublicationGLOBAL CHANGE BIOLOGY
ISSN1354-1013
Volume25Issue:3Pages:938-953
Corresponding AuthorHe, Honglin(hehl@igsnrr.ac.cn) ; Ren, Xiaoli(renxl@igsnrr.ac.cn)
AbstractIt is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback.
Keywordcarbon sequestration climate sensitivity non-steady state steady state turnover time
DOI10.1111/gcb.14547
WOS KeywordMEAN RESIDENCE TIME ; OLD-GROWTH FORESTS ; SOIL CARBON ; TERRESTRIAL CARBON ; GLOBAL PATTERNS ; TEMPERATURE SENSITIVITY ; EDDY COVARIANCE ; SPIN-UP ; SPATIAL-PATTERNS ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFC0500204] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19020301] ; National Natural Science Foundation of China[41571424] ; National Natural Science Foundation of China[31700417]
Funding OrganizationNational Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China
WOS Research AreaBiodiversity & Conservation ; Environmental Sciences & Ecology
WOS SubjectBiodiversity Conservation ; Ecology ; Environmental Sciences
WOS IDWOS:000459456700013
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/49422
Collection中国科学院地理科学与资源研究所
Corresponding AuthorHe, Honglin; Ren, Xiaoli
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
4.Univ Edinburgh, Sch GeoSci, Edinburgh, Midlothian, Scotland
5.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China
6.Univ Minnesota, Large Lakes Observ, Duluth, MN 55812 USA
7.No Arizona Univ, Ctr Ecosyst Sci & Soc Ecoss, Flagstaff, AZ 86011 USA
8.No Arizona Univ, Dept Biol Sci, Box 5640, Flagstaff, AZ 86011 USA
9.Chinese Acad Sci, Inst Bot, Beijing, Peoples R China
10.Chinese Acad Sci, Inst Appl Ecol, Shenyang, Liaoning, Peoples R China
11.Chinese Acad Sci, South China Bot Garden, Guangzhou, Guangdong, Peoples R China
12.Chinese Acad Sci, Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengla, Peoples R China
13.Chinese Acad Sci, Chengdu Inst Biol, Chengdu, Sichuan, Peoples R China
Recommended Citation
GB/T 7714
Ge, Rong,He, Honglin,Ren, Xiaoli,et al. Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation[J]. GLOBAL CHANGE BIOLOGY,2019,25(3):938-953.
APA Ge, Rong.,He, Honglin.,Ren, Xiaoli.,Zhang, Li.,Yu, Guirui.,...&Lin, Luxiang.(2019).Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation.GLOBAL CHANGE BIOLOGY,25(3),938-953.
MLA Ge, Rong,et al."Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation".GLOBAL CHANGE BIOLOGY 25.3(2019):938-953.
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