## 1. Introduction

[2] The use of eddy covariance (EC) has improved our understanding of temporally and spatially integrated net ecosystem exchange rates (*NEE*) of CO_{2} between ecosystems and the atmosphere, identifying key biotic and abiotic processes that control these rates [*Loescher et al.*, 2003; *Bowling et al.*, 2001; *Katul et al.*, 1998; *Lee*, 1998; *Goulden et al.*, 1996; *Grace et al.*, 1996; *Hollinger et al.*, 1994] and improving ecosystem process models [e.g., *Thornton et al.*, 2002; *Law et al.*, 2002; *Williams et al.*, 2001; *Aber et al.*, 1996]. As the EC technique matures, efforts are focused on reducing uncertainty in *NEE* estimates.

[3] The EC technique was pioneered over grass and croplands with long fetch and short roughness lengths [*Kaimal and Wyngaard*, 1990; *Verma et al.*, 1989; *Lemon*, 1960; *Monteith and Szeicz*, 1960]. Researchers have since applied this approach over structurally complex ecosystems in nonideal terrain, introducing new challenges in the interpretation of results and reduction of uncertainties. The EC technique is a direct, nondestructive micrometeorological approach derived through the simplification of the conservation equation [*Baldocchi*, 2003; *Baldocchi and Meyers*, 1998; *Baldocchi et al.*, 2000; *Shen and Leclerc*, 1997]. EC is used to estimate *NEE* through the addition of above-canopy turbulent exchange and the change in CO_{2} storage in the canopy air space (i.e., the temporal change in carbon concentration integrated from ground level to the point of measured turbulent exchange, term I, equation (1a)):

where *c* is the scalar quantity such as CO_{2}, *u*, *v*, and *w* are horizontal, cross-wind, and vertical windspeeds (m s^{−1}), respectively, *x*, *y*, and *z* are Cartesian coordinates, *t* is time, and *Z* is the measurement height (m), the overbars indicate a time average and prime denotes turbulent fluctuations (i.e., deviations from a mean quantity). Term I in equation (1a) represents the time rate-of-change of *c* in the vertical column (i.e., storage), and is considered to be equal to zero over a 24-hour period, but can be significant over shorter time intervals. Terms II–IV represent the turbulent flux divergence. Terms V–VII represent advection through the layer between the surface and sensor. The partial derivatives usually are estimated from data at points in the domain, and thus are replaced here by finite differences. Assuming that the measurement height is sufficient and the surface characteristics are horizontally homogeneous, terms III and IV are often thought to be 0 and ignored. Terms IV–VII are inherently difficult to measure well, but thought to be small and thus not often estimated. Hence only terms I and II remain. Term II in equation (1b) is commonly referred to as the eddy covariance (additional assumptions in its estimation are discussed later). *NEE* is typically estimated over a 30-min period from high-frequency measurements, and integrated further to estimate daily, season and annual fluxes. The relative importance in the sources of error differs across timescales, spatial extent and site conditions. For example, for *NEE* at minute-to-hour timescales (compare with photosynthetic uptake and respiratory efflux), systematic and random errors associated with instrumentation precision, calibration and placement, and turbulent transport become significant. In scaling *NEE* from day-to-season (months), random errors are reduced, but other errors are added because of filtering and filling gaps in data. Consequently, the sources of error in applying EC to understand rapid biological response to meteorological variables are quite different from those errors associated with determining the environmental controls on phenology, annual carbon exchange, effects from disturbance, or whether mature forested ecosystems continue to accumulate carbon. Of course the relative contribution of all errors becomes large in either example (i.e., minute-to-hour and day-to-season) when the mean flux is close to 0.

[4] Some of these errors or uncertainties stem from flows not fully accounted for within and below the sensor field, which can result in potential violation of the assumptions. Other uncertainties result from the stochastic nature of turbulence. A key stratagem of the long-term flux network, AmeriFlux, is to assure accurate estimates within and among flux sites for synthesis activities and regional analysis. The AmeriFlux science plan has outlined rigorous quality control protocols for within site measurements (see http://public.ornl.gov/ameriflux/About/scif.cfm) to avoid large systematic biases so that subtle spatial and temporal trends may be discerned. Quality assurance across the network is assessed through direct comparison of software routines and instrumentation. Using an independent raw data file developed by the Euroflux and AmeriFlux networks (“gold files” for closed- and open-path infrared gas analyzers can be found at http://public.ornl.gov/ameriflux/standards-gold.shtml), researchers can process flux data sets through their own software routine and check estimates against a standard processed file. Site-to-site differences among instrument configurations are estimated by an independent portable flux measurement system that visits each site. Consistency and rigor in sample design, analysis, diagnostics, and data quality checks help to ensure data are comparable across sites.

[5] In this paper, we review the current progress in estimating ecosystem level carbon exchange using the eddy covariance approach across different temporal scales, and suggest future research directions that can be enhanced by the EC technique. Explicit discussion of instrumentation errors and calibrations are not covered here, and are given by *Foken and Oncley* [1995], *Massman and Lee* [2002], and *Foken et al.* [2004], and related discussions on surface energy balance closure are given by *Aubinet et al.* [2000], *Mahrt* [1998], *Twine et al.* [2000], and *Foken et al.* [2004].