Expanding and Diversifying Continental-Scale Spring Phenology Models

Keywords: phenology, spring, modeling, climatology
Session Type: Virtual Paper Abstract
Day: Sunday
Session Start / End Time: 2/27/2022 11:20 AM (Eastern Time (US & Canada)) - 2/27/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 56

Abstract

The seasonal timing, or phenology, of many North American plant species is in flux with accelerating environmental change. The influence of factors such as temperature and precipitation on plant growth, as weather patterns change from spring to summer, vary from place-to-place, month-to-month, and for different plants species. An improved understanding of these relationships revealed by this research will support development of more accurate and diverse models of spring plant growth stages. These models may then be able to predict which trees and shrubs will be favored in different regions with future environmental change, to the benefit of many types of societal planning. Further, the project will implement national-scale, long-lead forecasts for new measures representing the spring season, which will also be relevant for annual agricultural, horticultural, and forestry management planning.
By combining recently developed weather and climate forecasts and robust statistical post-processing techniques with rich ground-based phenological data from the National Ecological Observatory Network (NEON) and other large-scale networks, this project will: (1) develop and refine models of spring plant phenological activity for dozens of species; (2) evaluate the influence of changes in climate driver variables over the course of the spring season—resulting from changes in the structure of large-scale circulation patterns—on predicting phenological events; (3) determine which species exhibit varying phenological response across spatial gradients, and therefore should be accounted for in predictive models; and (4) assess the potential predictability of those models on seasonal to decadal time horizons to guide long-term forecast operationalizing by the USA-NPN.