Modeling the phenological response to climate change and its impact on carbon cycle in Northeastern U.S. forests

Abstract

By controlling the timing of leaf activities, vegetation phenology plays an important role in regulating photosynthesis and other ecosystem processes. As driven by environmental variables, vegetation phenology has been shifting in response to climate change. The shift in vegetation phenology, in turn, exerts various feedbacks to affect the climate system. The magnitude of phenological change and the feedbacks has yet been well understood. The goal of this dissertation is to use phenological model with remote sensing and climate data to quantify historical and future trends in leaf onset and offset in northeastern U.S. forests, and use a dynamic ecosystem model, Agro-IBIS, to quantify the impact of phenological change on terrestrial carbon balance. This dissertation has three major parts. First, six phenological metrics based on remotely sensed vegetation index were evaluated with ground phenological observation in Agro-IBIS. Second, a modified phenological metric was used to parameterize a set of phenological models at regional scale; one model for each of leaf onset and offset were selected to examine historical trends; Agro-IBIS simulations were run to quantify the impact of phenological trends on ecosystem productivities. Finally, downscaled climate projections from global climate models under two emission scenarios were used to drive phenological models to predict the trends in leaf onset and offset in the 21st century; and the impact of photoperiod on leaf onset were particularly examined. The results of this study suggest that remotely sensed phenological metrics can be used to improve phenological models with evaluation and adjustment; advancement of leaf onset and delay of leaf offset in the past have increased productivities and could potentially mitigate the warming temperature in the future; lack of physiological understanding of the driving factors of phenology such as photoperiod could result in large uncertainties in phenological projections.