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This paper presents a brief overview and history of “phenoclimatology”, a subdiscipline of climatology, emphasizing atmosphere-biosphere interactions. Here, we describe the establishment and recent growth in models and forecasts created using in situ phenology observations and the factors enabling these advancements, with focus on North America. Most notably, large-scale phenological models paved the way for development of synthetic indices. Such indices can supply an assessment of a location’s general phenological response over a standard period, context for comparing regional or local-scale studies, the ability to analyze changes in damage risks for plants, and reconstruction of the timing of events in years past across many regions. As such, synthetic phenological indices have seen wide adoption in estimating spring-season evolution in real time, anticipating short-term impacts of an early or late start to spring, and in assessing changes in the timing of seasonal transitions associated with climate change.
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This content will become publicly available on January 15, 2026
Building spring development indices for woody species in the conterminous United States
Phenological indices are an effective approach for assessing spatial and temporal patterns and variability in plant development. The Spring Indices (SI-x), two widely adopted phenological indices, have been used in recent decades to predict development of woody plants, and document changes in spring growth timing, especially in North America. However, these two indices (Leaf and Bloom) capture only two “moments” in the continuum of spring when quantities of thermal or photo/thermal energy, associated with seasonal events in plants, are accumulated, limiting their utility to characterize the remainder of the spring season. Further, the Spring Indices do not account for intraspecific variation, limiting their ability to reflect non-cloned plant development. To address these shortcomings, we developed a novel suite of phenological indices that encompass a broader span of the spring season. These indices were constructed using observations contributed to the USA National Phenology Network’s Nature’s Notebook platform across many non-cloned tree and shrub species’ ranges, thereby incorporating differing regional responses within species due to genetic variations. Individual species model predictions of leaf or bloom timing exhibited an average mean absolute error of 8.55 days; most were improved by the inclusion of site-specific latitude, elevation, or 30-year average temperature. Leaf and bloom model outputs for individual species across the spring season were temporally aggregated into four leaf and bloom groups to produce a suite of Spring Development Indices (SDI). Accuracy of the SDI predictionswas 0.89 days lower, on average, than the species models, but 2.65 days better than SI-x. Generally, all SDIs were highly correlated. The SDIs exhibiting the most difference from the others were Early leaf, Very Early bloom, and Late bloom. As such, these SDIs provide novel insights, beyond SI-x, into the relative timing of spring-season “moments” across species in space and time.
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- Award ID(s):
- 2017831
- PAR ID:
- 10639352
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Agricultural and forest meteorology
- ISSN:
- 0168-1923
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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