Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-17-0357.1 |
Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques | |
Zhou, Sha1,16; Liang, Junyi2; Lu, Xingjie2,3,7; Li, Qianyu2,4; Jiang, Lifen3,7; Zhang, Yao2,5; Schwalm, Christopher R.6,7; Fisher, Joshua B.8; Tjiputra, Jerry9; Sitch, Stephen10; Ahlstrom, Anders11,12; Huntzinger, Deborah N.13,14; Huang, Yuefei1,15; Wang, Guangqian1; Luo, Yiqi3,4,7 | |
2018-04-01 | |
发表期刊 | JOURNAL OF CLIMATE
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2018 |
卷号 | 31期号:7页码:2833-2851 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; USA; Norway; England; Sweden |
英文摘要 | Terrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Inter-comparison Project (CMIP5) (historical simulations), Trends in Net Land-Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000429456500016 |
WOS关键词 | PROGRAM MULTISCALE SYNTHESIS ; TERRESTRIAL BIOSPHERE MODEL ; PRIMARY PRODUCTIVITY NPP ; COMPARING GLOBAL-MODELS ; EARTH SYSTEM MODELS ; AIR CO2 ENRICHMENT ; INTERCOMPARISON PROJECT ; CLIMATE-CHANGE ; INTERANNUAL VARIABILITY ; FOREST PRODUCTIVITY |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/19407 |
专题 | 气候变化 |
作者单位 | 1.Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing, Peoples R China; 2.Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA; 3.No Arizona Univ, Dept Biol Sci, Flagstaff, AZ USA; 4.Tsinghua Univ, Dept Earth Syst Sci, Beijing, Peoples R China; 5.Univ Oklahoma, Ctr Spatial Anal, Norman, OK 73019 USA; 6.Woods Hole Res Ctr, POB 296, Woods Hole, MA 02543 USA; 7.No Arizona Univ, Ctr Ecosyst Sci & Soc, Flagstaff, AZ USA; 8.CALTECH, Jet Prop Lab, Pasadena, CA USA; 9.Bjerknes Ctr Climate Res, Uni Res Climate, Bergen, Norway; 10.Univ Exeter, Coll Life & Environm Sci, Exeter, Devon, England; 11.Stanford Univ, Dept Earth Syst Sci, Stanford, CA 94305 USA; 12.Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden; 13.No Arizona Univ, Sch Earth Sci & Environm Sustainabil, Flagstaff, AZ USA; 14.No Arizona Univ, Dept Civil Engn Construct Management & Environm E, Flagstaff, AZ USA; 15.Qinghai Univ, Coll Ecol & Environm Engn, Qinghai, Peoples R China; 16.Columbia Univ, Dept Earth & Environm Engn, New York, NY 10027 USA |
推荐引用方式 GB/T 7714 | Zhou, Sha,Liang, Junyi,Lu, Xingjie,et al. Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques[J]. JOURNAL OF CLIMATE,2018,31(7):2833-2851. |
APA | Zhou, Sha.,Liang, Junyi.,Lu, Xingjie.,Li, Qianyu.,Jiang, Lifen.,...&Luo, Yiqi.(2018).Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques.JOURNAL OF CLIMATE,31(7),2833-2851. |
MLA | Zhou, Sha,et al."Sources of Uncertainty in Modeled Land Carbon Storage within and across Three MIPs: Diagnosis with Three New Techniques".JOURNAL OF CLIMATE 31.7(2018):2833-2851. |
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