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

Andrew D Richardson,David Y Hollinger,George G Burba,Kenneth J Davis,Lawrence B Flanagan,Gabriel G Katul,J William Munger,Daniel M Ricciuto,Paul C Stoy,Andrew E Suyker,Shashi B Verma,Steven C Wofsy

doi:10.1016/j.agrformet.2006.01.007

Abstract

Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO 2 ( FCO 2) 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 FCO 2, 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 ( FCO 2). For FCO 2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO 2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used.

Highlights

Measurements of surface-atmosphere fluxes of carbon and energy at eddy covariance sites around the world have provided important insight into how different ecosystems function in relation to abiotic environmental forcings (Baldocchi et al, 2001)
We focus on the random error, but note that a complete description of total flux measurement error requires a quantification of the systematic error or bias (Goulden et al, 1996; Moncrieff et al, 1996)
SF(l) is the standard deviation of the random flux measurement error for a flight leg of length l, rwc is the correlation coefficient between the vertical wind velocity w, and scalar c, tl is the integral lengthscale for c, a is the flight altitude of the aircraft, and zi is the height of the boundary layer

Summary

Introduction

Measurements of surface-atmosphere fluxes of carbon and energy at eddy covariance sites around the world have provided important insight into how different ecosystems function in relation to abiotic environmental forcings (Baldocchi et al, 2001). Since eddy covariance data are increasingly being assimilated with terrestrial ecosystem models (e.g., Braswell et al, 2005; Knorr and Kattge, 2005; Raupach et al, 2005), a systematic characterization of flux data uncertainties is needed. We focus on the random error, but note that a complete description of total flux measurement error requires a quantification of the systematic error or bias (Goulden et al, 1996; Moncrieff et al, 1996). This latter task seems to be especially difficult because if we knew the bias we probably could correct it. Systematic errors, which cannot be evaluated with the approach we use here, are discussed elsewhere (e.g., Baldocchi, 2003)

Methods

Results

Conclusion