干旱区生态环境知识资源中心(Arid): Predictability of tropical vegetation greenness using sea surface temperatures*

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DOI	10.1088/2515-7620/ab178a
	Predictability of tropical vegetation greenness using sea surface temperatures*
	Yan, Binyan; Mao, Jiafu; Shi, Xiaoying; Hoffman, Forrest M.; Notaro, Michael; Zhou, Tianjun; Mcdowell, Nate; Dickinson, Robert E.; Xu, Min; Gu, Lianhong; Ricciuto, Daniel M.
通讯作者	Mao, JF (corresponding author), Oak Ridge Natl Lab, Div Environm Sci, POB 2008, Oak Ridge, TN 37831 USA. ; Mao, JF (corresponding author), Oak Ridge Natl Lab, Climate Change Sci Inst, Oak Ridge, TN 37831 USA.
来源期刊	ENVIRONMENTAL RESEARCH COMMUNICATIONS
ISSN	2515-7620
出版年	2019
卷号	1 期号:3
英文摘要	Much research has examined the sensitivity of tropical terrestrial ecosystems to various environmental drivers. The predictability of tropical vegetation greenness based on sea surface temperatures (SSTs), however, has not been well explored. This study employed fine spatial resolution remotely-sensed Enhanced Vegetation Index (EVI) and SST indices from tropical ocean basins to investigate the predictability of tropical vegetation greenness in response to SSTs and established empirical models with optimal parameters for hindcast predictions. Three evaluation metrics were used to assess the model performance, i.e., correlations between historical observed and predicted values, percentage of correctly predicted signs of EVI anomalies, and percentage of correct signs for extreme EVI anomalies. Our findings reveal that the pan-tropical EVI was tightly connected to the SSTs over tropical ocean basins. The strongest impacts of SSTs on EVI were identified mainly over the arid or semi-arid tropical regions. The spatially-averaged correlation between historical observed and predicted EVI time series was 0.30 with its maximum value reaching up to 0.84. Vegetated areas across South America (25.76%), Africa (33.13%), and Southeast Asia (39.94%) were diagnosed to be associated with significant SST-EVI correlations (p < 0.01). In general, statistical models correctly predicted the sign of EVI anomalies, with their predictability increasing from similar to 60% to nearly 100% when EVI was abnormal (anomalies exceeding one standard deviation). These results provide a basis for the prediction of changes in greenness of tropical terrestrial ecosystems at seasonal to intra-seasonal scales. Moreover, the statistics-based observational relationships have the potential to facilitate the benchmarking of Earth System Models regarding their ability to capture the responses of tropical vegetation growth to long-term signals of oceanic forcings.
英文关键词	predictability tropical vegetation greenness sea surface temperatures
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000561423400001
WOS关键词	SEMIARID ECOSYSTEMS ; CLIMATE VARIABILITY ; RAINFALL ANOMALIES ; SEASONAL RAINFALL ; INDIAN-OCEAN ; EL-NINO ; PRECIPITATION ; ATLANTIC ; TELECONNECTIONS ; PATTERNS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	中国科学院大气物理研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/369881
作者单位	[Yan, Binyan; Dickinson, Robert E.] Univ Texas Austin, Jackson Sch Geosci, Austin, TX 78712 USA; [Mao, Jiafu; Shi, Xiaoying; Gu, Lianhong; Ricciuto, Daniel M.] Oak Ridge Natl Lab, Div Environm Sci, POB 2008, Oak Ridge, TN 37831 USA; [Mao, Jiafu; Shi, Xiaoying; Hoffman, Forrest M.; Xu, Min; Gu, Lianhong; Ricciuto, Daniel M.] Oak Ridge Natl Lab, Climate Change Sci Inst, Oak Ridge, TN 37831 USA; [Hoffman, Forrest M.; Xu, Min] Oak Ridge Natl Lab, Comp Sci & Math Div, Oak Ridge, TN 37831 USA; [Notaro, Michael] Univ Wisconsin, Nelson Inst Ctr Climat Res, Madison, WI 53796 USA; [Zhou, Tianjun] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing 100029, Peoples R China; [Mcdowell, Nate] Pacific Northwest Natl Lab, Richland, WA 99352 USA
推荐引用方式 GB/T 7714	Yan, Binyan,Mao, Jiafu,Shi, Xiaoying,et al. Predictability of tropical vegetation greenness using sea surface temperatures*[J]. 中国科学院大气物理研究所,2019,1(3).
APA	Yan, Binyan.,Mao, Jiafu.,Shi, Xiaoying.,Hoffman, Forrest M..,Notaro, Michael.,...&Ricciuto, Daniel M..(2019).Predictability of tropical vegetation greenness using sea surface temperatures*.ENVIRONMENTAL RESEARCH COMMUNICATIONS,1(3).
MLA	Yan, Binyan,et al."Predictability of tropical vegetation greenness using sea surface temperatures*".ENVIRONMENTAL RESEARCH COMMUNICATIONS 1.3(2019).