Oak Ridge National Laboratory’s Terrestrial Ecosystem Science Science Focus Area: A 2023 Overview
Paul J. Hanson* (firstname.lastname@example.org), Daniel M. Ricciuto, TES SFA Project Participants
Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN
Understanding fundamental responses and feedbacks of terrestrial ecosystems to climatic and atmospheric change is the aim of the Oak Ridge National Laboratory’s (ORNL) Terrestrial Ecosystem Science (TES) SFA. The proposed research efforts of the ORNL TES SFA seek to provide answers to the following overarching question: How vulnerable to climate change are carbon (C) stores of eastern North American ecosystems, and what are the implications for C–climate feedbacks? The TES SFA focuses on eastern United States ecosystems vulnerable to water cycle and energy changes whose impacts are highly uncertain in Earth system models. Proposed science includes manipulations, multidisciplinary observations, database compilation, and fundamental process studies integrated and iterated with modeling activities. The dominant manipulation is the SPRUCE experiment testing responses to multiple levels of warming at ambient and elevated CO2 for a Picea-Sphagnum peatland ecosystem. Long-term observations of ecosystem function at the Missouri Ozarks AmeriFlux (MOFLUX) eddy covariance site provide the opportunity to characterize ecosystem response to dominant hydrologic limitations. Further process-level work occurs at smaller scales and aims to improve mechanistic representation of processes within terrestrial biosphere models by furthering scientific understanding of fundamental ecosystem functions and their response to environmental change. The TES SFA integrates experimental and observational studies with model building, parameter estimation, and evaluation to yield reliable model projections. This integrated model-experiment approach focuses on improving the E3SM Land Model (ELM) and fosters enhanced, interactive, and mutually beneficial engagement between models and experiments.