A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes |
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| Institution: | 1. Complex Systems Research Center, University of New Hampshire, Morse Hall, 39 College Road, Durham, NH 03824, USA;2. NE Research Station, USDA Forest Service, 271 Mast Road, Durham, NH 03824, USA;3. LI-COR Biosciences, Inc., 4421 Superior Street, Lincoln, NE 68504, USA;4. Department of Meteorology, Penn State University, 512 Walker Building, University Park, PA 16802, USA;5. Department of Biological Sciences, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta, Canada T1K 3M4;6. Nicholas School of the Environment and Earth Sciences, Duke University, Box 90328, Durham, NC 27708, USA;7. Division of Engineering and Applied Science/Department of Earth and Planetary Science, Harvard University, Cambridge, MA 02138, USA;8. School of Natural Resources, University of Nebraska-Lincoln, P.O. Box 830728, Lincoln, NE 68583, USA;1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, 210023 Nanjing, China;2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;3. Max Planck Institute for Biogeochemistry, Hans Knöll Straße 10, Jena D-07745, Germany;4. Helmholtz Center Potsdam, GFZ German Research Center for Geosciences, Remote Sensing Section, Telegrafenberg A17, 14473 Potsdam, Germany;5. Department of Geography and Program in Planning, University of Toronto, Toronto, ON M5S 3G3, Canada;6. LMD/IPSL, CNRS, ENS, PSL Research University, Ecole polytechnique, Université Paris Saclay, UPMC Univ Paris 06, Sorbonne Universités, 91128 Palaiseau, France;1. Ecosystem Science Division, Department of Environmental Science, Policy and Management, University of California at Berkeley, Berkeley, CA 94720, USA;2. Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland;1. Department of Anthropology, University of Washington, Box 353100, Seattle, WA 98195-3100, USA;2. The Center for Social Science Computation and Research (CSSCR), University of Washington, Box 353345, Seattle, WA 98195-3345, USA;1. Institute of Water Sciences, College of Engineering, Peking University, Beijing, 100871, China;2. Department of Earth and Environmental Engineering, Columbia University, New York, New York, 10027, USA;3. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China;4. Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China;1. RTI International, Research Triangle Park, NC, USA;2. Cochrane Austria, Danube University Krems, Krems, Austria;3. Genetics Department, Victor Babes University of Medicine and Pharmacy, Timisoara, Romania;4. Copenhagen Trial Unit; Centre for Clinical Intervention Research, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark |
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| Abstract: | Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO2 (FCO2) represent the “true” flux plus or minus potential random and systematic measurement errors. Here, we use data from seven sites in the AmeriFlux network, including five forested sites (two of which include “tall tower” instrumentation), one grassland site, and one agricultural site, to conduct a cross-site analysis of random flux error. Quantification of this uncertainty is a prerequisite to model-data synthesis (data assimilation) and for defining confidence intervals on annual sums of net ecosystem exchange or making statistically valid comparisons between measurements and model predictions.We differenced paired observations (separated by exactly 24 h, under similar environmental conditions) to infer the characteristics of the random error in measured fluxes. Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian), distribution, and increase as a linear function of the magnitude of the flux for all three scalar fluxes. Across sites, variation in the random error follows consistent and robust patterns in relation to environmental variables. For example, seasonal differences in the random error for H are small, in contrast to both LE and FCO2, for which the random errors are roughly three-fold larger at the peak of the growing season compared to the dormant season. Random errors also generally scale with Rn (H and LE) and PPFD (FCO2). For FCO2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used. |
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