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Detection of Carbon Use Efficiency Extremes and Analysis of Their Forming Climatic Conditions on a Global Scale Using a Remote Sensing-Based Model

文献类型：期刊论文

作者Wang, Miaomiao2,3; Zhao, Jian2; Wang, Shaoqiang1,3,4
刊名REMOTE SENSING
出版日期2022-10-01
卷号14期号:19页码:13
关键词carbon use efficiency extreme events gross primary production climate conditions ecosystem model
DOI10.3390/rs14194873
通讯作者Zhao, Jian(zhaojian@faas.cn)
英文摘要Carbon use efficiency (CUE) represents the proficiency of plants in transforming carbon dioxide (CO2) into carbon stock in terrestrial ecosystems. CUE extremes represent ecosystems' extreme proficiency in carbon transformation. Studying CUE extremes and their forming climate conditions is critical for enhancing ecosystem carbon storage. However, the study of CUE extremes and their forming climate conditions on the global scale is still lacking. In this study, we used the results from the daily Boreal Ecosystem Productivity Simulator (BEPS) model to detect the positive and negative CUE extremes and analyze their forming climatic conditions on a global scale. We found grasslands have the largest potential in changing global CUE, with the contribution being approximately 32.4% to positive extremes and 30.2% to negative extremes. Spring in the Northern Hemisphere (MAM) contributed the most (30.5%) to positive CUE extremes, and summer (JJA) contributed the most (29.7%) to negative CUE extremes. The probabilities of gross primary production (GPP) extremes resulted in CUE extremes (>25.0%) being larger than autotrophic respiration (Ra), indicating CUE extremes were mainly controlled by GPP rather than Ra extremes. Positive temperature anomalies (0 similar to 1.0 degrees C) often accompanied negative CUE extreme events, and positive CUE extreme events attended negative temperature anomalies (-1.0 similar to 0 degrees C). Moreover, positive (0 similar to 20.0 mm) and negative precipitation (-20.0 similar to 0 mm) anomalies often accompanied positive and negative CUE extremes, respectively. These results suggest that cooler and wetter climate conditions could be beneficial to enhance carbon absorptions of terrestrial ecosystems. The study provides new knowledge on proficiency in carbon transformation by terrestrial ecosystems.
WOS关键词GROSS PRIMARY PRODUCTION ; PRIMARY PRODUCTIVITY ; TERRESTRIAL ECOSYSTEMS ; CHINA ; TEMPERATE ; SATELLITE ; RESPONSES ; DRIVERS ; IMPACTS ; EVENTS
资助项目National Ecosystem Science Data Center[NESDC20210301] ; Collaborative Innovation Project of High-Quality Development of Agriculture in Fujian Province[XTCXGC2021015] ; Fujian Intelligent Agricultural Science and Technology Innovation Team[CXTD2021013-1] ; Fujian Academy of Agricultural Sciences Free Exploration Science and Technology Innovation Project[ZYTS202233]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000867246500001
出版者MDPI
资助机构National Ecosystem Science Data Center ; Collaborative Innovation Project of High-Quality Development of Agriculture in Fujian Province ; Fujian Intelligent Agricultural Science and Technology Innovation Team ; Fujian Academy of Agricultural Sciences Free Exploration Science and Technology Innovation Project
源URL[http://ir.igsnrr.ac.cn/handle/311030/185572]  
专题中国科学院地理科学与资源研究所
通讯作者Zhao, Jian
作者单位1.China Univ Geosci, Sch Geog & Informat Engn, Beijing 430074, Peoples R China
2.Fujian Acad Agr Sci, Inst Digital Agr, Fuzhou 350003, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100045, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Miaomiao,Zhao, Jian,Wang, Shaoqiang. Detection of Carbon Use Efficiency Extremes and Analysis of Their Forming Climatic Conditions on a Global Scale Using a Remote Sensing-Based Model[J]. REMOTE SENSING,2022,14(19):13.
APA Wang, Miaomiao,Zhao, Jian,&Wang, Shaoqiang.(2022).Detection of Carbon Use Efficiency Extremes and Analysis of Their Forming Climatic Conditions on a Global Scale Using a Remote Sensing-Based Model.REMOTE SENSING,14(19),13.
MLA Wang, Miaomiao,et al."Detection of Carbon Use Efficiency Extremes and Analysis of Their Forming Climatic Conditions on a Global Scale Using a Remote Sensing-Based Model".REMOTE SENSING 14.19(2022):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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