Nom du corpus

Corpus Systématique Végétale

Titre du document

Ecological forecasting under climatic data uncertainty: a case study in phenologicalmodeling

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Éditeur
IOP
Langue(s) du document
Anglais
Type de document
Article
Mots-clés d'auteur
  • ecology
  • climate
  • forecasting
Nom du fichier dans la ressource
Syst_veg6_v2_006772
Auteur(s)
  • Benjamin I Cook 1,2
  • Adam Terando 3
  • Allison Steiner 4
Affiliation(s)
  • 1) NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
  • 2) Lamont-Doherty Earth Observatory, 61 Route 9W, Palisades, NY 10964, USA
  • 3) Biodiversity and Spatial Information Center, NC State University, Raleigh, NC 27695-7617, USA
  • 4) Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI 48109-2143, USA
Résumé

Forecasting ecological responses to climate change represents a challenge to theecological community because models are often site-specific and climate data arelacking at appropriate spatial and temporal resolutions. We use a case studyapproach to demonstrate uncertainties in ecological predictions related to the drivingclimatic input data. We use observational records, derived observational datasets(e.g.interpolated observations from local weather stations and gridded data products) andoutput from general circulation models (GCM) in conjunction with site basedphenology models to estimate the first flowering date (FFD) for three woodyflowering species. Using derived observations over the modern time period, wefind that cold biases and temperature trends lead to biased FFD simulations forall three species. Observational datasets resolved at the daily time step resultin better FFD predictions compared to simulations using monthly resolution.Simulations using output from an ensemble of GCM and regional climate models overmodern and future time periods have large intra-ensemble spreads and tend tounderestimate observed FFD trends for the modern period. These results indicate thatcertain forcing datasets may be missing key features needed to generate accuratehindcasts at the local scale (e.g.trends, temporal resolution), and that standardmodeling techniques (e.g.downscaling, ensemble mean, etc) may not necessarilyimprove the prediction of the ecological response. Studies attempting to simulatelocal ecological processes under modern and future climate forcing therefore needto quantify and propagate the climate data uncertainties in their simulations.

Catégories INIST
  • 1 - sciences appliquees, technologies et medecines
  • 2 - sciences exactes et technologie
  • 3 - terre, ocean, espace
  • 4 - geophysique externe
Catégories Scopus
  • 1 - Health Sciences ; 2 - Medicine ; 3 - Public Health, Environmental and Occupational Health
  • 1 - Physical Sciences ; 2 - Environmental Science ; 3 - General Environmental Science
  • 1 - Physical Sciences ; 2 - Energy ; 3 - Renewable Energy, Sustainability and the Environment
Catégories WoS
  • 1 - science ; 2 - meteorology & atmospheric sciences
  • 1 - science ; 2 - environmental sciences
Identifiant ISTEX
963A51B3EB1B5C44F8CAE3BF5F1CB50D53EEB4A2
Revue

Environmental Research Letters

Année de publication
2010
Présence de XML structuré
Oui
Score qualité du texte
8.576
Version PDF
1.4
Type de publication
Journal
ark:/67375/0T8-6DNZ1VQ0-0
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