6月21日：Complex Carbon Cycle Phenomena Governed by Simple Rules
发布者：未知
发布时间：2019-06-21
浏览量：368
主 讲 人：骆亦其（教授）
主 持 人：夏建阳（教授）
开始时间：2019年6月21日（周五）10:00
讲座地址：闵行资环楼148室
主办单位：生态与环境科学学院、科技处
报告人简介：
Dr. Yiqi Luo is a Professor in the Department of Biological Sciences and director of the EcoLab in Center for Ecosystem Science and Society, Northern Arizona University. Among the most notable of Dr. Luo’s research is his work on terrestrial carbon and nitrogen cycles in addition to his pioneering research on data assimilation and ecological forecasts. Presently, he is leading major international initiatives on incorporating experimental and observational data into models to constrain their forecasts of future changes in ecosystem services. His team has conducted field experiments, computational modeling and theoretical studies of carbon and nitrogen dynamics in terrestrial ecosystems. He has authored more than 400 publications, with total citations of >31,000 and H-index of 92, on ecosystem ecology, ecological modelling and global-change biology. He has been editor for a number of journals, such as Global Change Biology and Ecological Applications. Over the past 20 years he has trained more than 100 graduate students, post-doctoral associates, undergraduate students and international scholars. He has been elected a Fellow of the American Association for the Advancement of Science (AAAS), the American Geophysical Union (AGU), and the Ecological Society of America (ESA).
报告内容简介:
Global carbon cycle has been extensively studied in the past decades via observation through various networks, experiments in field and laboratory, and simulation models. Even so, models yield highly uncertain predictions, experimental results are often contradictory to each other, and observation generates highly variable data. In this talk, I will analyze fundamental processes of carbon cycle in the terrestrial ecosystems and reveal simple rules that govern land carbon cycle. Those rules not only can facilitate our understanding of land carbon cycle but also can be used to accelerate research by modeling, experimentation and observation.
