Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation
文献类型：期刊论文
| 作者 | Ge, Rong6; He, Honglin5; Ren, Xiaoli; Zhang, Li5; Yu, Guirui5; Smallman, T. Luke; Zhou, Tao3; Yu, Shi-Yong; Luo, Yiqi1,11; Xie, Zongqiang10 |
| 刊名 | GLOBAL CHANGE BIOLOGY
 |
| 出版日期 | 2019 |
| 卷号 | 25期号:3页码:938-953 |
| ISSN号 | 1354-1013 |
| 关键词 | carbon sequestration climate sensitivity non-steady state steady state turnover time |
| DOI | 10.1111/gcb.14547 |
| 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 | Biodiversity Conservation ; Ecology ; Environmental Sciences |
| 电子版国际标准刊号 | 1365-2486 |
| 出版地 | HOBOKEN |
| 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 |
| 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] |
| 源URL | [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.Univ Minnesota, Large Lakes Observ, Duluth, MN 55812 USA 2.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Beijing, Peoples R China 3.Univ Edinburgh, Sch GeoSci, Edinburgh, Midlothian, Scotland 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 7.Chinese Acad Sci, Inst Bot, Beijing, Peoples R China 8.Chinese Acad Sci, Chengdu Inst Biol, Chengdu, Sichuan, Peoples R China 9.Chinese Acad Sci, Key Lab Trop Forest Ecol, Xishuangbanna Trop Bot Garden, Mengla, Peoples R China 10.No Arizona Univ, Dept Biol Sci, Box 5640, Flagstaff, AZ 86011 USA |
| 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. |
入库方式： OAI收割
来源：植物研究所
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