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法来量化陆地碳固存。在世界各地数百个地点进行的涡旋通量测量都显示出陆地碳汇强度的年际变化（即年际变化）。该项目旨在确定二氧化碳净生态系统交换的季节和年际变化（SIAV）的潜在原因，这些变化是由构成AmeriFlux和Fluxnet涡流通量网络的许多长期微气象站点测量的。虽然以前的努力侧重于对观测到的单个地点二氧化碳净生态系统交换的季节和年际变化的机制理解，但试图根据广泛生态系统的数据确定季节和年际变化的更一般描述或模型的工作要少得多。该项目将采用多种方法，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异模式，并开发基于过程的模型来预测季节和年际变异。考虑到世界正在对涡流通量数据进行大量投资，考虑到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，这项研究的更广泛影响在于这项工作的综合性质。

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.