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

By Andrew D. Richardson, David Y. Hollinger, G. Burba, Kenneth J. Davis, L. Flanagan, Gabriel G. Katul, J. Munger, D. Ricciuto, P. Stoy, A. Suyker and 2 more. 2006

488 citations recorded by the source corpus.

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.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/272b52e4b46d083222ce8e2ab1007e79f5e72863

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Citations in the corpus

Linking flux network measurements to continental scale simulations: ecosystem carbon dioxide exchange capacity under non‐water‐stressed conditions (2007, background): “Recent analyses (Hollinger & Richardson, 2005; Richardson et al., 2006) indicate that flux data measurement errors are not Gaussian, but are instead characterized by a peaked distribution with long tails.”
Corrections and data quality control (2012, background): “Due to the presentation by Stull (1988) of the correction of humidity-dependent fluctuations of the specific heat proposed by Brook (1978), which is some percentage of the flux, this correction is often used.”
Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes (2007, methodology): “Each technique (Falge et al., 2001; Hollinger et al., 2004; Barr et al., 2004; Desai et al., 2005; Richardson et al., 2006b; Noormets et al., 2007) uses one equation for the ecosystem respiration (ER) and one equation for the light response of the ecosystem, which is the gross primary production…”
‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems (2008, background): “%) as the datasets reach a year in duration (Moncrieff et al. 1996; Loescher et al. 2006; Oren et al. 2006; Richardson et al. 2006).”
A new methodology to interpret high resolution measurements of net carbon fluxes between terrestrial ecosystems and the atmosphere (2012, methodology): “If the data follows a Laplace rather than a Gaussian distribution, not the RMSE but the standard deviation SDev should be used as a measure of the model residuals (Richardson et al., 2006; Lasslop et al., 2008): SDev = √ 2 ·MAE = √ 2 · 1 N ∑ |mi − ti|, (3.”
The energy balance closure problem: an overview. (2008, background): “In more recent studies, random errors based on the comparison of measurements made with at least two systems installed within a short distance were investigated (Finkelstein and Sims 2001, Richardson et al. 2006), but these errors are not of a size to solve the closure problem.”
Use of digital webcam images to track spring green-up in a deciduous broadleaf forest (2007, methodology): “A separate set of model parameters was fit to each day’s data, and maximum likelihood parameter estimates were obtained using weighted absolute deviations optimization (see Hollinger and Richardson 2005; Richardson et al. 2006b).”