Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation

doi:10.1111/gcb.14547

CORC > 成都生物研究所 > 中国科学院成都生物研究所 > 生物多样性与生态系统服务领域 > 生态研究

	Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation
	Ge, Rong 4,10; He, Honglin 4,7; Ren, Xiaoli 4; Zhang, Li 4,7; Yu, Guirui 4,7; Smallman, T. Luke 4; Zhou, Tao 9; Yu, Shi-Yong 12; Luo, Yiqi 5; Xie, Zongqiang 11
刊名	GLOBAL CHANGE BIOLOGY
	2019
卷号	25 期号:3 页码:938-953
关键词	carbon sequestration climate sensitivity non-steady state steady state turnover time
ISSN号	1354-1013
DOI	10.1111/gcb.14547
产权排序	12
文献子类	Article
英文摘要	It 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.
学科主题	Environment/ecology
URL标识	查看原文
WOS关键词	MEAN RESIDENCE TIME ; OLD-GROWTH FORESTS ; SOIL CARBON ; TERRESTRIAL CARBON ; GLOBAL PATTERNS ; TEMPERATURE SENSITIVITY ; EDDY COVARIANCE ; SPIN-UP ; SPATIAL-PATTERNS ; MODEL
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
语种	英语
出版者	WILEY
WOS记录号	WOS:000459456700013
内容类型	期刊论文
源URL	[http://210.75.237.14/handle/351003/31005]
专题	生物多样性与生态系统服务领域_生态研究
作者单位	1.Chinese Acad Sci, Inst Appl Ecol, Shenyang, Liaoning, Peoples R China; 2.Chinese Acad Sci, South China Bot Garden, Guangzhou, Guangdong, Peoples R China; 3.Chinese Acad Sci, Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengla, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China; 5.No Arizona Univ, Ctr Ecosyst Sci & Soc Ecoss & Dept Biol Sci, Box 5640, Flagstaff, AZ 86011 USA; 6.Univ Edinburgh, Sch GeoSci, Edinburgh, Midlothian, Scotland; 7.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 8.Chinese Acad Sci, Chengdu Inst Biol, Chengdu, Sichuan, Peoples R China 9.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China; 10.Univ Chinese Acad Sci, Beijing, Peoples R China;
推荐引用方式 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.