Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 13-26 |
| Number of pages | 14 |
| Journal | Global Change Biology |
| Volume | 27 |
| Issue number | 1 |
| Early online date | 19 Oct 2020 |
| DOIs | |
| Publication status | Published - Jan 2021 |
Bibliographical note
Funding Information:We are grateful to Kristina Anderson‐Teixeira and Mingkai Jiang for their reviews which improved this manuscript to a great extent. We further thank Gab Abramowitz, Veronika Eyring, Michael Fienen, Andy Fox, Yuan Gao, Birgit Hassler, Xin Huang, Randall Hunt, Lifen Jiang, and Jeremy White for their helpful overview on example cyberinfrastructure tools. The PEcAn project which organized the workshop where the authors of this paper came together is supported by the NSF (ABI no. 1062547, ABI no. 1458021, ABI no. 1457897, ABI no. 1062204, DIBBS no. 1261582), NASA Terrestrial Ecosystems, the Energy Biosciences Institute, and an Amazon AWS education grant. We would also like to thank Boston University for providing the venue for the workshop that inspired this article. I.F. and T.V. acknowledge funding from the Strategic Research Council at the Academy of Finland (decision 327214), the Academy of Finland (decision 297350), and Business Finland (decision 6905/31/2018) to the Finnish Meteorological Institute. T.Q. is funded by the UK NERC National Centre for Earth Observation. J.B.F. contributed to this work from the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. J.B.F. was supported in part by NASA programs: CARBON and CMS. S.P.S. was partially supported by NASA CMS (grant #80NSSC17K0711), and through the DOE Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the Earth & Environmental Systems Modeling (EESM) Program in the Climate and Environmental Sciences Division (CESD), and the Next‐Generation Ecosystem Experiments (NGEE‐Arctic and NGEE‐Tropics) supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, as well as through the United States Department of Energy contract no. DE‐SC0012704 to Brookhaven National Laboratory. M.D.K. acknowledges funding from the Australian Research Council (ARC) Centre of Excellence for Climate Extremes (CE170100023), the ARC Discovery Grant (DP190101823) and support from the NSW Research Attraction and Acceleration Program. F.M.H. was partially supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, which is managed by UT‐Battelle, LLC, for the U.S. Department of Energy under contract DE‐AC05‐00OR22725. Additional support was provided by the Data Program, by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA) in the Earth & Environmental Systems Modeling (EESM) Program, and by the Next‐Generation Ecosystem Experiments (NGEE‐Arctic and NGEE‐Tropics) Projects in the Terrestrial Ecosystem Science (TES) Program. The Data, EESM, and TES Programs are part of the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science.
Funding Information:
We are grateful to Kristina Anderson-Teixeira and Mingkai Jiang for their reviews which improved this manuscript to a great extent. We further thank Gab Abramowitz, Veronika Eyring, Michael Fienen, Andy Fox, Yuan Gao, Birgit Hassler, Xin Huang, Randall Hunt, Lifen Jiang, and Jeremy White for their helpful overview on example cyberinfrastructure tools. The PEcAn project which organized the workshop where the authors of this paper came together is supported by the NSF (ABI no. 1062547, ABI no. 1458021, ABI no. 1457897, ABI no. 1062204, DIBBS no. 1261582), NASA Terrestrial Ecosystems, the Energy Biosciences Institute, and an Amazon AWS education grant. We would also like to thank Boston University for providing the venue for the workshop that inspired this article. I.F. and T.V. acknowledge funding from the Strategic Research Council at the Academy of Finland (decision 327214), the Academy of Finland (decision 297350), and Business Finland (decision 6905/31/2018) to the Finnish Meteorological Institute. T.Q. is funded by the UK NERC National Centre for Earth Observation. J.B.F. contributed to this work from the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. California Institute of Technology. J.B.F. was supported in part by NASA programs: CARBON and CMS. S.P.S. was partially supported by NASA CMS (grant #80NSSC17K0711), and through the DOE Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the Earth & Environmental Systems Modeling (EESM) Program in the Climate and Environmental Sciences Division (CESD), and the Next-Generation Ecosystem Experiments (NGEE-Arctic and NGEE-Tropics) supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, as well as through the United States Department of Energy contract no. DE-SC0012704 to Brookhaven National Laboratory. M.D.K. acknowledges funding from the Australian Research Council (ARC) Centre of Excellence for Climate Extremes (CE170100023), the ARC Discovery Grant (DP190101823) and support from the NSW Research Attraction and Acceleration Program. F.M.H. was partially supported by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. Additional support was provided by the Data Program, by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA) in the Earth & Environmental Systems Modeling (EESM) Program, and by the Next-Generation Ecosystem Experiments (NGEE-Arctic and NGEE-Tropics) Projects in the Terrestrial Ecosystem Science (TES) Program. The Data, EESM, and TES Programs are part of the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science.
Publisher Copyright:
© 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd
Keywords
- accessibility
- benchmarking
- community cyberinfrastructure
- data
- data assimilation
- ecosystem models
- interoperability
- reproducibility