Controls on spatial and temporal variability in vegetation phenology

 

The term phenology describes both the seasonal rhythms of plants and animals, and the study of these rhythms (J110). Plant phenological processes, including, for example, when leaves emerge in the spring and change color in the autumn, are highly responsive to variation in weather (e.g. a warm vs. cold spring) as well as longer-term changes in climate (e.g. warming trends and changes in the timing and amount of rainfall) (J113, J105, J054, J028). By influencing surface-atmosphere fluxes of carbon, water, and energy, phenology also plays a critical role in mediating vegetation feedbacks to the climate system (J094, J047). We recently completed an invited review paper (J110) that highlights major knowledge gaps and uncertainties in the field of terrestrial vegetation phenology, particularly with respect to climate change impacts and feedbacks to climate.

 

The Richardson Lab’s PhenoCam project (http://phenocam.unh.edu), funded through NSF’s Macrosystems Biology program, uses networked, digital cameras (“webcams”) to monitor vegetation phenology at a continental scale (J097, J096, J066, J058, J035). Our underlying goal is to investigate relationships between phenology, ecosystem processes, and climate change (J104). Specific questions are as follows: (i) How do photoperiod, temperature, and precipitation govern phenological transitions in different plant functional types at local, regional and continental scales? (J095, J087); (ii) How will vegetation phenology respond to rising temperatures and changing precipitation regimes, and what are the associated uncertainties? (J105, J003, J028); and (iii) What are the forecasted impacts on ecosystem services related to carbon and water, at regional and continental scales, of these phenological shifts (J104, J101)?

 

Model-data fusion plays a central role in this project (J112, J105, J003). Using camera-derived phenological metrics as constraints, we will estimate the probability distributions of parameters for different phenological models, and characterize prediction uncertainties. We will use formal model selection criteria (J029) to test and evaluate different model structures. To assess the nature and magnitude of climate change impacts on phenology and ecosystem function, models will be run forward using IPCC climate projections to produce continental-scale forecasts. The theory, and associated predictive models, that we develop through this work will improve the representation of both phenology and interactions between phenology and surface-atmosphere exchanges of carbon, water, and energy in terrestrial biosphere models (J105, J101, J094). These improvements are essential to improve predictive skill and reduce uncertainties in future climate projections (J110).

 

This is a highly interdisciplinary project that leverages team expertise in biometeorology, process modeling, remote sensing, and computer vision, and involves collaboration with the National Ecological Observatory Network (NEON) and the National Center for Atmospheric Research (NCAR), as well as the USA National Phenology Network (USA-NPN), and researchers from the University of New Hampshire, Boston University, and Washington University in St. Louis.

 

 

Phenology controls many feedbacks between vegetation and the climate system.