A generalized, bioclimatic index to predict foliar phenology in response to climate

William M. Jolly, Ramakrishna Nemani, Steven W. Running

Research output: Contribution to journalArticlepeer-review

354 Scopus citations


The phenological state of vegetation significantly affects exchanges of heat, mass, and momentum between the Earth's surface and the atmosphere. Although current patterns can be estimated from satellites, we lack the ability to predict future trends in response to climate change. We searched the literature for a common set of variables that might be combined into an index to quantify the greenness of vegetation throughout the year. We selected as variables: daylength (photoperiod), evaporative demand (vapor pressure deficit), and suboptimal (minimum) temperatures. For each variable we set threshold limits, within which the relative phenological performance of the vegetation was assumed to vary from inactive (0) to unconstrained (1). A combined Growing Season Index (GSI) was derived as the product of the three indices. Ten-day mean GSI values for nine widely dispersed ecosystems showed good agreement (r > 0.8) with the satellite-derived Normalized Difference Vegetation Index (NDVI). We also tested the model at a temperate deciduous forest by comparing model estimates with average field observations of leaf flush and leaf coloration. The mean absolute error of predictions at this site was 3 days for average leaf flush dates and 2 days for leaf coloration dates. Finally, we used this model to produce a global map that distinguishes major differences in regional phenological controls. The model appears sufficiently robust to reconstruct historical variation as well as to forecast future phenological responses to changing climatic conditions.

Original languageEnglish
Pages (from-to)619-632
Number of pages14
JournalGlobal Change Biology
Issue number4
StatePublished - Apr 2005


  • Climate change
  • Global
  • Minimum temperature
  • Model
  • Phenology
  • Photoperiod
  • Vapor pressure deficit


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