Finnish Meteorological Institute, Helsinki, Finland.
Department of Earth and Environment, Boston University, Boston, MA, USA.
Glob Chang Biol. 2021 Jan;27(1):13-26. doi: 10.1111/gcb.15409. Epub 2020 Nov 6.
In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is a key to meeting the pressing needs of science and society in the 21st century.
在快速全球化的时代，我们理解和预测地球自然系统的能力落后于我们监测和测量生物圈变化的能力。将观测结果纳入模型的瓶颈限制了我们充分利用日益增长的大量和多样化可用数据的能力。在这里，我们批判性地审视了将生态系统建模和测量工作联系起来的信息基础设施，并提出了一条社区网络基础设施发展的路线图，可以减少经验研究和建模之间的分歧，并加速发现的步伐。数据-模型集成的新时代需要投资于可访问、可扩展和透明的工具，这些工具整合了整个社区的专业知识，包括建模者和经验主义者。本路线图重点介绍了社区工具的五个关键机会：社区网络基础设施的基础；数据摄入；模型对数据的校准；模型-数据基准测试；以及数据同化和生态预测。这种由社区驱动的方法是满足 21 世纪科学和社会紧迫需求的关键。
