Climate change science has witnessed an explosion in the amount and types of data that can be brought to bear on the potential responses of the terrestrial carbon cycle and biodiversity to global change. Many of the most pressing questions about global change are not necessarily limited by the need to collect new data as much as by our ability to synthesize existing data. This project specifically seeks to improve this ability. Because no one measurement provides a complete picture, multiple data sources must be integrated in a sensible manner. Process-based models represent an ideal framework for integrating these data streams because they represent multiple processes at different spatial and temporal scales in ways that capture our current understanding of the causal connections across scales and among data types. Three components are required to bridge this gap between the available data and the required level of understanding: 1) a state-of-the-art ecosystem model, 2) a workflow management system to handle the numerous streams of data, and 3) a data assimilation statistical framework in order to synthesize the data with the model.
Acknowledgements: The PEcAn project is supported by the National Science Foundation (ABI #1062547, ABI #1458021, DIBBS #1261582, ARC #1023477, EF #1318164, EF #1241894, EF #1241891), NASA Terrestrial Ecosystems, the Department of Energy (ARPA-E awards #DE-AR0000594 and DE-AR0000598), the Energy Biosciences Institute, and an Amazon AWS in Education Grant.
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