Plant life in U.S. forests consume large volumes of carbon dioxide, playing an important role in regulating the concentrations of this important gas in the atmosphere. There is an urgent need for scientists to reduce uncertainties in future projections of atmospheric carbon dioxide concentrations and to more confidently assess whether plants in forests will continue to offset carbon dioxide emissions to the atmosphere from the burning of fossil fuels. The NSF-funded National Ecological Observatory Network (NEON) is a continental-scale ecological observation facility that collects and provides quality-controlled data from 81 field sites across the United States that characterize and quantify how our nation's ecosystems are changing. NEON's measurements of plant photosynthesis and respiration are providing the data needed for computer projections of continental scale patterns, year-to-year variations, and trends in the global carbon cycle. This project will develop more refined continental-scale carbon profiles by using a novel approach to improve complex computer models and delivering a widely-sought application of NEON data. The investigators will broaden impacts of this project by providing training to early-career scientists, broadening diversity, sharing products with the research community, and enhancing research and education infrastructure. The results are anticipated to be of high value for integrated assessments of global change and will be useful for federal, state and local agencies, and land managers who are making decisions on managing U.S. natural resources.
The investigators will constrain the continental-scale terrestrial carbon cycle by integrating observations from NEON, satellite data, data-driven methods, and data-model integration techniques. The objectives are to: (1) use NEON data to develop continental-scale flux products that are need to help realize NSF's goals for NEON; (2) evaluate information content of NEON data sets for reducing model uncertainty; (3) assimilate multiple NEON data sets into complex land models to quantify the U.S. land carbon sink and its uncertainty; (4) understand the continental-scale carbon dynamics and underlying regulatory mechanisms. First, the investigators will utilize NEON data to develop an hourly, gridded Gross Primary Production(GPP) product with uncertainty estimates based on satellite observations and meteorological data. Second, they will use NEON data along with solar-induced chlorophyll fluorescence (SIF) data from satellites to develop gridded complimentary GPP products with uncertainty estimates. Third, they will utilize multiple heterogeneous datasets from NEON and the gridded GPP products for quantifying the US land carbon sink potential and assess the effectiveness of NEON data to constrain parameter estimation and model prediction. Fourth, they will develop a multiple model ensemble to understand constraints of flux- vs. pool-based data on structure uncertainty in model prediction. Fifth, they will apply data assimilation techniques to quantify parameter uncertainty and assess constraints of multiple vs. single NEON data sets on parameter estimation with three different models. Finally, they will assess the US land carbon sink, its uncertainty, and regulatory mechanisms with NEON data-based gridded flux products, NEON data-trained models, and the traceability framework. This project will ultimately provide feedback towards improvement of models and NEON observations via uncertainty analysis.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
