ABSTRACT

Records of Earth?s past climates (e.g., from tree rings, marine sediments, ice, and corals) are key to understanding what the climate system is capable of and for testing the climate models used to project future climate. At present, there are a number of social and technical barriers that prevent the full use of these paleoclimate observations to inform modeling. PaleoCube proposes to lower these barriers by bringing scientists of diverse perspectives to work in the Cloud. The data and code will be shared through hackathons, webinars, and YouTube tutorials designed to engage scientists involved in different aspects of climate science on an even playing field while building capacity among the geoscientific workforce.

PaleoCube will use and extend upon existing cyberinfrastructure (LinkedEarth, Pangeo, Jupyter), emerging data standards, and the scientific Python ecosystem (including an extension to the pandas library to accommodate more general time representations) to bring scientists to the data and associated reproducible workflows stored in the cloud. The proposed activities will make these tools accessible, facilitate interoperability with the Scientific Python Stack, and build a large library of reproducible scientific workflows that inexperienced users can emulate and modify to serve their own purposes. By providing easy and free access to interactive computing at scale, coupled with didactic examples and hackathons, PaleoCube will broaden participation in the geosciences to under-represented groups, enrich the STEM pipeline, and provide transferable data science skills to geoscience practitioners and enthusiasts.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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