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NSF Summer 2019 workshop: Computing Arctic Data: Orono, ME - Spring 2019


Project start
Project end
Type of project
Project theme
Education & Outreach
Project topic
Education & Outreach

Project details

Science / project summary

To better understand causes, trends and thresholds of a changing Arctic and to develop robust societal adaptation strategies new research tools are needed. There is a wealth of past climate data sets stored in the NSF-funded Arctic Data Center (ADC) that are unresolved yet have the potential to expand and deepen the current state of understanding about how the Arctic is responding to environmental changes. This award supports an integrative workshop that will bring together a diverse group of early career scientists and experts from the fields of ice core, computer and climate sciences to transform the existing research data computation platform. The goal is to pave the way for the development of a future generation of computer tools necessary to better understand complex interactions of multiple driving forces that are changing Earth's environment. Objectives are to evaluate the latest computational advances, break existing interdisciplinary barriers that limit the use of ice core data sets in climate research, and, by openly sharing results, promote the development of future products that will benefit the Arctic research community and the global population. Evaluating present and forecasting future trends in the "New Arctic" system is closely connected to understanding multidimensional paleoclimate data archives. Using open source tools to ease the reproduction of computational and data-intensive portions of paleoclimate research, transparency in data processing steps will increase. In addition, (1) the range of usability of existing climate and ice core based paleoclimate data archives will be extended; (2) open access data and software libraries, utilizing open source based software tools will be developed; (3) paleoclimate and computer infrastructure specific white papers will be developed that will summarize the state of the problem, map future pathways for systematic improvements, and finally converge this rapidly evolving research domain with a novel computational and easy to use data processing framework.