A Multi-Site Analysis of Random Error in Tower-Based Measurements of Carbon and Energy Fluxes

Authors

Andrew D. Richardson, University of New Hampshire, DurhamFollow
David Y. Hollinger, NE Research Station, USDA Forest Service
George C. Burba, LI-COR Biosciences, Lincoln, NebraskaFollow
Kenneth J. Davis, Pennsylvania State UniversityFollow
Lawrence B. Flanagan, University of Lethbridge
Gabriel G. Katul, Duke University
J. William Munger, Harvard University
Daniel M. Ricciuto, University of Nebraska-Lincoln
Paul C. Stoy, Duke University
Andrew E. Suyker, University of Nebraska-LincolnFollow
Shashi Verma, University of Nebraska-LincolnFollow
Steven C. Wofsy, Harvard University

Document Type

Article

Date of this Version

3-16-2006

Citation

Agricultural and Forest Meteorology 136 (2006): 1-18.

Comments

U.S. government work.

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.