DigitalCommons@University of Nebraska - Lincoln - Andrew D. Richardson, David Y. Hollinger, George C. Burba, Kenneth J. Davis, Lawrence B. Flanagan, Gabriel G. Katul, J. William Munger, Daniel M. Ricciuto, Paul C. Stoy, Andrew E. Suyker, Shashi Verma, and Steven C. Wofsy: A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes

Papers in Natural Resources

Title

A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes

Authors

Andrew D. Richardson, Complex Systems Research Center, University of New Hampshire, Morse Hall, 39 College Road, Durham, NH 03824, USA
David Y. Hollinger, NE Research Station, USDA Forest Service, 271 Mast Road, Durham, NH 03824, USA
George C. Burba, LI-COR Biosciences, Inc., 4421 Superior Street, Lincoln, NE 68504, USA
Kenneth J. Davis, Department of Meteorology, Penn State University, 512 Walker Building, University Park, PA 16802, USA
Lawrence B. Flanagan, Department of Biological Sciences, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta, Canada T1K 3M4
Gabriel G. Katul, Nicholas School of the Environment and Earth Sciences, Duke University, Box 90328, Durham, NC 27708, USA
J. William Munger, Division of Engineering and Applied Science/Department of Earth and Planetary Science, Harvard University, Cambridge, MA 02138, USA
Daniel M. Ricciuto, University of Nebraska - Lincoln
Paul C. Stoy, Nicholas School of the Environment and Earth Sciences, Duke University, Box 90328, Durham, NC 27708, USA
Andrew E. Suyker, University of Nebraska - Lincoln
Shashi Verma, University of Nebraska - Lincoln
Steven C. Wofsy, Harvard University, Cambridge, MA 02138, USA

Document Type

Article

Date of this Version

March 2006

Comments

Published in Agricultural and Forest Meteorology 136 (2006) 1–18.

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 R_(n (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.