IB-CAS  > 植被与环境变化国家重点实验室
Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation
Ge, Rong1; He, Honglin2; Ren, Xiaoli; Zhang, Li2; Yu, Guirui2; Smallman, T. Luke; Zhou, Tao4; Yu, Shi-Yong; Luo, Yiqi6,7; Xie, Zongqiang8; Wang, Silong9; Wang, Huimin; Zhou, Guoyi10; Zhang, Qibin8; Wang, Anzhi9; Fan, Zexin11; Zhang, Yiping11; Shen, Weijun10; Yin, Huajun12; Lin, Luxiang11
2019
发表期刊GLOBAL CHANGE BIOLOGY
ISSN1354-1013
卷号25期号:3页码:938-953
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.
关键词carbon sequestration climate sensitivity non-steady state steady state turnover time
学科领域Biodiversity Conservation ; Ecology ; Environmental Sciences
DOI10.1111/gcb.14547
收录类别SCI
语种英语
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
WOS记录号WOS:000459456700013
出版者WILEY
文献子类Article
出版地HOBOKEN
EISSN1365-2486
资助机构National Key Research and Development Program of China [2016YFC0500204] ; Strategic Priority Research Program of the Chinese Academy of SciencesChinese Academy of Sciences [XDA19020301] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41571424, 31700417]
作者邮箱hehl@igsnrr.ac.cn ; renxl@igsnrr.ac.cn
作品OA属性Green Submitted
引用统计
被引频次:31[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ibcas.ac.cn/handle/2S10CLM1/19610
专题植被与环境变化国家重点实验室
作者单位1.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
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.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
gcb.14547.pdf(1877KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Ge, Rong]的文章
[He, Honglin]的文章
[Ren, Xiaoli]的文章
百度学术
百度学术中相似的文章
[Ge, Rong]的文章
[He, Honglin]的文章
[Ren, Xiaoli]的文章
必应学术
必应学术中相似的文章
[Ge, Rong]的文章
[He, Honglin]的文章
[Ren, Xiaoli]的文章
相关权益政策
暂无数据
收藏/分享
文件名: gcb.14547.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。