Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration,Global Change Biology

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Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration
Global Change Biology ( IF 10.8 ) Pub Date : 2020-10-19 , DOI: 10.1111/gcb.15409
Istem Fer ₁ , Anthony K Gardella _{2,

3} , Alexey N Shiklomanov ₄ , Eleanor E Campbell ₅ , Elizabeth M Cowdery ₂ , Martin G De Kauwe _{6,

7,

8} , Ankur Desai ₉ , Matthew J Duveneck ₁₀ , Joshua B Fisher ₁₁ , Katherine D Haynes ₁₂ , Forrest M Hoffman _{13,

14} , Miriam R Johnston ₁₅ , Rob Kooper ₁₆ , David S LeBauer ₁₇ , Joshua Mantooth ₁₈ , William J Parton ₁₉ , Benjamin Poulter ₄ , Tristan Quaife ₂₀ , Ann Raiho ₂₁ , Kevin Schaefer ₂₂ , Shawn P Serbin ₂₃ , James Simkins ₂₄ , Kevin R Wilcox ₂₅ , Toni Viskari ₁ , Michael C Dietze ₂

Affiliation

Finnish Meteorological Institute, Helsinki, Finland.
Department of Earth and Environment, Boston University, Boston, MA, USA.
School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA.
Biospheric Sciences Laboratory (618), NASA Goddard Space Flight Center, Greenbelt, MD, USA.
Earth Systems Research Center, University of New Hampshire, Durham, NH, USA.
ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia.
Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia.
Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW, Australia.
Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Harvard Forest, Harvard University, Petersham, MA, USA.
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA.
Computational Earth Sciences Group and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA.
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
NCSA (National Center for Supercomputing Applications), University of Illinois at Urbana Champaign, Urbana, IL, USA.
College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA.
The Fulton School at St. Albans, St. Albans, MO, USA.
Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA.
UK National Centre for Earth Observation and Department of Meteorology, University of Reading, Reading, UK.
Fish, Wildlife, and Conservation Biology Department, Colorado State University, Fort Collins, CO, USA.
National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA.
Brookhaven National Laboratory, Environmental and Climate Sciences Department, Upton, NY, USA.
University of Delaware, Newark, DE, USA.
Ecosystem Science and Management, University of Wyoming, Laramie, WY, USA.

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.

中文翻译：

超越生态系统建模：生态数据模型集成的社区网络基础设施路线图

在全球快速变化的时代，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。通过观察为模型提供信息的瓶颈降低了我们充分利用不断增长的数量和种类的可用数据的能力。在这里，我们对连接生态系统建模和测量工作的信息基础设施进行了批判性的审视，并提出了社区网络基础设施发展的路线图，该路线图可以减少实证研究和建模之间的分歧，并加快发现的步伐。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，这些工具集成了整个社区（包括建模者和经验主义者）的专业知识。该路线图重点关注社区工具的五个关键机会：社区网络基础设施的底层基础；数据摄取；模型与数据的校准；模型数据基准测试；以及数据同化和生态预测。这种社区驱动的方法是满足 21 世纪科学和社会迫切需求的关键。

更新日期：2020-12-09

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