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Making data useful for modelers to understand complex Earth systems

机译:使数据对建模者有用以了解复杂的地球系统

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摘要

The Arctic is changing rapidly with dramatic local and global effect. To understand that change requires understanding the Arctic as a system. Models of different processes and at various scales are necessary tools for analyzing and understanding the Earth system. Models are extremely diverse, yet they all require quality data. Through a series of case studies, augmented with ethnographic observation around the International Polar Year, this work examines how modelers assess, acquire, and prepare data for their models. By comparing specific case studies, common themes emerge that can be compared against broader observation. These themes, in turn, suggest data management techniques or requirements for data systems to improve access and use by modelers and generally improve understanding of the Arctic system. The study has an Arctic focus because of the rapid changes occurring in the Arctic, but the approach and results should apply generally to Earth system science. This case study based approach has proven to be a useful method for teasing out both general and specific data needs for different models. An overarching lesson is that greater short-term benefit to modelers and significant gains in efficiency can be achieved by improving the formats, convention, and consistency of the data rather than improved interfaces and analysis tools. A “data-first” philosophy can improve the data systems that support the overall interdisciplinary, integrative science necessary to understand the complex Earth system.
机译:北极正在迅速变化,并产生了巨大的局部和全球影响。要了解这种变化,就需要了解北极作为一个系统。不同过程和规模的模型是分析和理解地球系统的必要工具。模型非常多样化,但是它们都需要高质量的数据。通过一系列案例研究,并在国际极地年前后对人种学进行了观察,这项工作研究了建模者如何评估,获取和准备模型数据。通过比较具体案例研究,可以发现可以与更广泛的观察结果进行比较的共同主题。这些主题反过来建议数据管理技术或对数据系统的要求,以改善建模人员的访问和使用并总体上增进对北极系统的了解。由于北极地区发生了迅速的变化,因此该研究的重点是北极,但是研究方法和结果应普遍适用于地球系统科学。这种基于案例研究的方法已被证明是解决不同模型的通用和特定数据需求的有用方法。一个总体的教训是,通过改进数据的格式,约定和一致性,而不是改进接口和分析工具,可以使建模人员获得更大的短期利益,并可以显着提高效率。 “数据优先”的理念可以改善支持理解复杂地球系统所必需的整体跨学科,综合科学的数据系统。

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