Empirical assessment of uncertainties of meteorological parameters and turbulent fluxes in the AmeriFlux network


Corresponding author: A. Schmidt, Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, Oregon 97331, USA. (Andres.Schmidt@oregonstate.edu)


[1] Terrestrial ecosystem-atmosphere exchange of carbon, water vapor, and energy has been measured for over a decade at many sites globally. To minimize measurement and analysis errors, quality assurance data have been collected over short periods along-side tower instruments at AmeriFlux research sites. Theoretical and empirical error and uncertainty values have been reported for various aspects of the eddy covariance technique but until recently it has not been possible to constrain network level variation based on direct comparison of side-by-side measurements. Paired observations, although rare in practice, offer a possibility to obtain real-world error estimates for flux observations and corresponding uncertainties. In this study, we report the relative instrumental errors from the AmeriFlux quality assurance and quality control (QA/QC) site intercomparisons of 84 site visits (2002–2012). Relative errors, including random and systematic instrumental errors, are presented for meteorological and radiation variables, gas concentrations, and the turbulent fluxes. The lowest relative errors (<2%) were found for the meteorological parameters, while the largest relative errors were found for latent heat and CO2 fluxes. The mean relative instrumental error for CO2 flux averaged −8.2% (underestimation by the tower instruments). Sensible and latent heat fluxes exhibited mean errors of −1.7% and −5.2%, respectively. Deviation around the mean was also largest for the turbulent fluxes, approaching 20%. Because the data collected during QA/QC site visits are used to identify and correct errors, our results represent a conservative estimate of instrumental errors in the AmeriFlux database. Overall, the presented results confirm the high quality of the network data and underline its status as a valuable data source for the research community.