Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration

Fer, I. ORCID: https://orcid.org/0000-0001-8236-303X, Gardella, A. K., Shiklomanov, A. N. ORCID: https://orcid.org/0000-0003-4022-5979, Campbell, E. E. ORCID: https://orcid.org/0000-0002-9272-6276, Cowdery, E. M., De Kauwe, M. G. ORCID: https://orcid.org/0000-0002-3399-9098, Desai, A., Duveneck, M. J., Fisher, J. B., Haynes, K. D., Hoffman, F. M., Johnston, M. R., Kooper, R., LeBauer, D. S., Mantooth, J., Parton, W., Poulter, B. ORCID: https://orcid.org/0000-0002-9493-8600, Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613, Raiho, A. ORCID: https://orcid.org/0000-0002-2552-3399, Schaefer, K., Serbin, S. P. ORCID: https://orcid.org/0000-0003-4136-8971, Simkins, J., Wilcox, K. R., Viskari, T. and Dietze, M. C. ORCID: https://orcid.org/0000-0002-2324-2518 (2020) Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration. Global Change Biology, 27 (1). pp. 13-26. ISSN 1365-2486 doi: 10.1111/gcb.15409

Abstract/Summary

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, 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 foundationsof community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is key to meeting the pressing needs of science and society in the 21st century.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/93658
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Wiley-Blackwell
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