Data-Model Fusion at AmeriFlux Sites: Towards Predictive Understanding of Seasonal and Interannual Variability in Net Ecosystem Exchange

为了了解陆地和大气之间的碳通量，科学家们广泛采用所谓的涡流通量方法来量化陆地碳固存。 全世界数百个地点的涡动通量测量都显示出强烈的逐年变化（即，年际变化）的强度陆地碳汇。 该项目旨在确定构成AmeriFlux和Fluxnet涡流通量网络的许多长期微气象站点测量的二氧化碳净生态系统交换的季节和年际变化的根本原因。 虽然以前的努力都集中在发展一个机械的理解观察到的季节性和年际变化的净生态系统交换二氧化碳在单一的网站，少得多的工作试图确定一个更一般的描述或模型的季节性和年际变化的基础上，从广泛的生态系统的数据。 该项目将使用多种方法，包括数据处理，统计分析，基于过程的建模和逆分析，以研究气候变化对生态系统过程的直接和间接影响，阐明不同生态系统类型的二氧化碳净生态系统交换的一般变化模式，并开发基于过程的模型来预测季节和年际变化。 这项研究的更广泛的影响在于工作的综合性质，考虑到世界对涡流通量数据的大量投资，以及这些数据集有可能大大提高我们对全球碳循环的认识并为政策制定提供信息。

To understand carbon fluxes between the land and the atmosphere, scientists have widely employed the so-called eddy-flux method to quantify land carbon sequestration. The eddy-flux measurements at hundreds of sites across the world all have showed strong year-to-year variation (i.e., interannual variability) in the strength of the terrestrial carbon sink. This project seeks to determine the underlying causes of the seasonal and interannual variation (SIAV) in net ecosystem exchange of carbon dioxide as measured by many long-term micrometeorological sites that make up the AmeriFlux and the Fluxnet eddy-flux networks. While previous efforts have focused on developing a mechanistic understanding of observed seasonal and interannual variability in net ecosystem exchange of carbon dioxide at single sites, far less work has attempted to identify a more general description or model for seasonal and interannual variability based on data from a broad range of ecosystems. This project will use multiple approaches, including data processing, statistical analysis, process-based modeling, and inverse analysis to examine the direct and indirect effects of climatic variation on ecosystem processes, to elucidate general patterns of variability in net ecosystem exchange of carbon dioxide across ecosystem types, and to develop a process-based model for predicting seasonal and interannual variability. The broader impacts of this research lie in the synthetic nature of the work, given the large investment the world is making in eddy flux data, and given the potential for these data sets to substantially improve our knowledge of the global carbon cycle and to inform policy making.