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Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration.
Fer, Istem; Gardella, Anthony K; Shiklomanov, Alexey N; Campbell, Eleanor E; Cowdery, Elizabeth M; De Kauwe, Martin G; Desai, Ankur; Duveneck, Matthew J; Fisher, Joshua B; Haynes, Katherine D; Hoffman, Forrest M; Johnston, Miriam R; Kooper, Rob; LeBauer, David S; Mantooth, Joshua; Parton, William J; Poulter, Benjamin; Quaife, Tristan; Raiho, Ann; Schaefer, Kevin; Serbin, Shawn P; Simkins, James; Wilcox, Kevin R; Viskari, Toni; Dietze, Michael C.
  • Fer I; Finnish Meteorological Institute, Helsinki, Finland.
  • Gardella AK; Department of Earth and Environment, Boston University, Boston, MA, USA.
  • Shiklomanov AN; School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA.
  • Campbell EE; Biospheric Sciences Laboratory (618), NASA Goddard Space Flight Center, Greenbelt, MD, USA.
  • Cowdery EM; Earth Systems Research Center, University of New Hampshire, Durham, NH, USA.
  • De Kauwe MG; Department of Earth and Environment, Boston University, Boston, MA, USA.
  • Desai A; ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia.
  • Duveneck MJ; Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia.
  • Fisher JB; Evolution & Ecology Research Centre, University of New South Wales, Sydney, NSW, Australia.
  • Haynes KD; Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA.
  • Hoffman FM; Harvard Forest, Harvard University, Petersham, MA, USA.
  • Johnston MR; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
  • Kooper R; Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA.
  • LeBauer DS; Computational Earth Sciences Group and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
  • Mantooth J; Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN, USA.
  • Parton WJ; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
  • Poulter B; NCSA (National Center for Supercomputing Applications), University of Illinois at Urbana Champaign, Urbana, IL, USA.
  • Quaife T; College of Agriculture and Life Sciences, University of Arizona, Tucson, AZ, USA.
  • Raiho A; The Fulton School at St. Albans, St. Albans, MO, USA.
  • Schaefer K; Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA.
  • Serbin SP; Biospheric Sciences Laboratory (618), NASA Goddard Space Flight Center, Greenbelt, MD, USA.
  • Simkins J; UK National Centre for Earth Observation and Department of Meteorology, University of Reading, Reading, UK.
  • Wilcox KR; Fish, Wildlife, and Conservation Biology Department, Colorado State University, Fort Collins, CO, USA.
  • Viskari T; National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA.
  • Dietze MC; Brookhaven National Laboratory, Environmental and Climate Sciences Department, Upton, NY, USA.
Glob Chang Biol ; 27(1): 13-26, 2021 01.
Article en En | MEDLINE | ID: mdl-33075199
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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Ecosistema / Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Ecosistema / Modelos Teóricos Tipo de estudio: Prognostic_studies Idioma: En Año: 2021 Tipo del documento: Article