Probabilistic Carbon Flux Upscaling Across a Northern Forest Ecoregion
Jingfeng
Xiao, University of New Hampshire, j.xiao@unh.edu
Kenneth
J
Davis, The Pennsylvania State University, kjd10@psu.edu
Kusum
J
Naithani, The Pennsylvania State University, kjn18@psu.edu
(Presenting)
Nathan
Urban, The Pennsylvania State University, nurban@psu
Klaus
Keller, The Pennsylvania State University, klaus@psu.edu
Ankur
Desai, University of Wisconsin, desai@aos.wisc.edu
Jiquan
Chen, University of Toledo, jiquan.chen@utoledo.edu
Asko
Noormets, North Carolina State University, anoorme@ncsu.edu
Kelly
Cherrey, The Pennsylvania State University, kcherrey@psu.edu
Bruce
Cook, NASA Goddard, bruce.cook@nasa.gov
Paul
Bolstad, University of Minnesota, pbolstad@umn.edu
Dong
Hua, University of Minnesota, dhua@umn.edu
Ryan
Anderson, University of Montana, ryan.anderson@ntsg.umt.edu,
Steven
Running, University of Montana, swr@ntsg.umt.edu
Nicanor
Saliendra, USDA Forest Service, rkolka@fs.fed.us
Randy
Kolka, USDA Forest Service, nsaliendra@fs.fed.us
Peter
Weishampel, University of Minnesota, peter.weishampel@gmail.com
Quantifying uncertainty in carbon flux upscaling is critical to improve our understanding of the terrestrial carbon cycle. The objectives of this project are to: 1) develop and test an improved algorithm for diagnoses of maps of net ecosystem carbon exchange (NEE) in northern temperate forests using a combination of in situ and remotely sensed data, 2) evaluate multiple sources of uncertainty, 3) determine the value of various observations in reducing this uncertainty, and 4) test the algorithm outside of the region where it was developed. The study is located in the upper Midwestern forests of the U.S., and utilizes the observations collected by the Chequamegon Ecosystem-Atmosphere Study (ChEAS). A simple diagnostic model for estimating NEE was developed and model parameters were optimized by assimilating eddy covariance flux observations. Estimates of NEE with 1 km2 spatial resolution were produced at a daily time step over the study region. The initial uncertainty estimates found that parameter uncertainties, selection and grouping of eddy covariance flux data, and the choice of land cover product all had substantial impacts on regional carbon budgets. Future work will include refined and expanded uncertainty estimates, extension of the algorithm beyond our study area, and evaluation of a similar approach using a process-based terrestrial carbon cycle model.
Presentation Type: Poster
Poster Session: Carbon Cycle Science
NASA TE Funded Awards Represented:
Davis, Kenneth
Probabilistic Carbon Flux Upscaling Across a Northern Forest Ecoregion