Arctic Field Projects

Project Title: NSF Summer 2019 workshop: Computing Arctic Data: Orono, ME - Spring 2019 (Award# 1848747)

PI: Kurbatov, Andrei V (
Phone: (207) 581.2840 
Institute/Department: U of Maine, Climate Change Institute 
IPY Project?
Funding Agency: US\Federal\NSF\GEO\OPP\ARC\ANS
Program Manager: Dr. Cynthia Suchman (
Discipline(s): | Education and Outreach |

Project Web Site(s):

Science 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.

Logistics Summary:
The workshop goal is to bring together a diverse group of researchers and graduate students that are using or would like to use ice core data available from the Arctic Data Center to produce the future generation of paleoclimate data sets. The goal is to learn about common research workflows that the community is using and to test the applicability of selected Arctic data sets and software tools to build the next generation of digital paleoclimate data products and reconstructions. In the past decade, the volume and complexity of data available in ice core science has grown rapidly. This increase has several major drivers: First, an increase in sampling frequency and quality has resulted in rapid generation of data. Second, advances in the Web and computer system integration of related technologies have simplified data exchange. Finally, the scientific community and funding agencies are supporting major efforts to increase research transparency by requiring researchers to publicly share project data collected during research in long term data repositories. All of these converging elements hold the potential to significantly boost scientific discovery due to the benefits of sharing data with other disciplines, but new computing approaches are needed. The 2019 Arctic Data Workshop on 14 May 2019 to 16 May 2019 at the University of Maine, in Orono, Maine will focus on evaluation and testing tools that simplify the use of publicly available Arctic data in a meaningful way. The goal is to increase the ease and transparency of working back from scientific results, for example a table within a publication, to the original datasets from which the summarized data were derived. By actively soliciting community wide input and experience, the workshop will incorporate and share back with the community related best practices from code development, digital curation, and software engineering. No fieldwork will be conducted.

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