The Terrestrial Ecosystem Science (TES) program seeks to improve the representation of terrestrial ecosystem processes in Earth system models, thereby improving the quality of climate model projections and providing the scientific foundation of solutions for the Department of Energy's (DOE) most pressing energy and environmental challenges. TES is managed within the Department of Energy (DOE) Office of Science’s Office of Biological and Environmental Research (BER).
The TES program develops unique, foundational scientific insights about the terrestrial biosphere’s role in the global cycling of carbon, nutrients, and water. The program also supports research examining the feedbacks between the terrestrial biosphere and Earth’s climate system. TES focuses on ecosystems and ecological processes that are globally or regionally significant, expected to be sensitive to climate change, and insufficiently understood or inadequately represented in models. As part of the Climate and Environmental Sciences Division, TES coordinates its activities with BER’s climate modeling program (and research from other federal agencies), ensuring that experimental and observational results are incorporated into Earth system models to improve climate projections.Learn More »
Within its broad research portfolio, TES works to advance priority research areas and coordinate with other federal programs with shared interest areas. Current examples of this cross-cutting structure include:
Focusing on critical areas of uncertainty in our understanding and/or model representation of the global ecosystem. Learn More »
Improving understanding of changes in the distribution and cycling of carbon among the land, ocean, and atmospheric reservoirs and how that understanding can be used to establish a scientific foundation for societal responses to global environmental change. Learn More »
Providing continuous observations of ecosystem-level exchanges of CO2, water, energy, and momentum spanning diurnal, synoptic, seasonal, and interannual time scales. Learn More »
Linking process studies and observations to model developement and evaluation of improved process representation, initialize multiscale model domains, calibrate models, and evaluate model predictions. Learn More »