Eddy covariance raw data processing for CO2 and energy fluxes calculation at ICOS ecosystem stations

Open access


The eddy covariance is a powerful technique to estimate the surface-atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO2, water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g. ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-the-art processing methods.

Aubinet M., Grelle A., Ibrom A., et al., 2000. Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Adv Ecol Res., 30, 113-175.

Aubinet M., Vesala T., and Papale D., 2012. Eddy covariance, a practical guide to measurement and data analysis, Springer.

Baldocchi D.D., Hincks B.B., and Meyers T.P., 1988. Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69(5), 1331-1340.

Baldocchi D.D., 2003. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob. Change Biol., 9(4), 479-492.

Barr A.G., Richardson A.D., Hollinger D.Y., et al., 2013. Use of change-point detection for friction-velocity threshold evaluation in eddy-covariance studies. Agr. Forest Meteorol., 171, 31-45.

Berger B.W., Davis K.J., Yi C., Bakwin P.S., and Zhao C.L., 2001. Long-term carbon dioxide fluxes from a very tall tower in a northern forest: Flux measurement methodology. J. Atmos. Ocean Tech., 18(4), 529-542.

Béziat P., Ceschia E., and Dedieu G., 2009. Carbon balance of a three crop succession over two cropland sites in South West France. Agr. Forest Meteorol., 149(10), 1628-1645.

BIPM, IEC, IFCC, ILAC, IUPAC, IUPAP, ISO and OIML, 2008. Evaluation of measurement data-guide for the expression of uncertainty in measurement. JCGM 100. Available at: http://www.bipm.org/en/publications/guides/gum.html

Clement R., 2004. Mass and energy exchange of a plantation forest in Scotland using micrometeorological methods. IAES. Edinburgh, The University of Edinburgh: 597.

Dragoni D., Schmid H.P., Grimmond C.S.B., and Loescher H.W., 2007. Uncertainty of annual net ecosystem productivity estimated using eddy covariance flux measurements. J. Geophys. Res.-Atmos., 112(D17).

Finkelstein P.L. and Sims P.F., 2001. Sampling error in eddy correlation flux measurements. J. Geophys. Res.-Atmos., 106, 3503-3509.

Finnigan J.J., 2004. A re-evaluation of long-term flux measurement techniques part II: coordinate systems. Boundary-Layer Meteorology, 113(1), 1-41.

Finnigan J.J., Clement R., Malhi Y., Leuning R., and Cleugh H.A., 2003. A re-evaluation of long-term flux measurement techniques - Part I: Averaging and coordinate rotation. Bound-Lay. Meteorol., 107, 1-48.

Foken T. and Wichura B., 1996. Tools for quality assessment of surface-based flux measurements. Agr. Forest Meteorol., 78: 83-105.

Fratini G., Ibrom A., Arriga N., Burba G., and Papale D., 2012. Relative humidity effects on water vapour fluxes measured with closed-path eddy-covariance systems with short sampling lines. Agr. Forest Meteorol., 165, 53-63.

Fratini G., McDermitt D.K., and Papale D., 2014. Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction. Biogeosciences, 11, 1037-1051.

Gash J.H.C. and Culf A.D., 1996. Applying a linear detrend to eddy correlation data in realtime. Bound-Lay. Meteorol., 79(3), 301-306.

Hollinger D.Y. and Richardson A.D., 2005. Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiol., 25(7), 873-885.

Horst T.W., 1997. A simple formula for attenuation of eddy fluxes measured with first-order-response scalar sensors. Boundary-Layer Meteorology, 82(2), 219-233.

Horst T.W. and Lenschow D.H., 2009. Attenuation of scalar fluxes measured with spatially-displaced sensors. Bound-Lay. Meteorol., 130(2), 275-300.

Hsieh C.I., Katul G., and Chi T.W., 2000. An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Adv. Water Resour., 23(7), 765-772.

Ibrom A., Dellwik E, Flyvbjerg H., Jensen N.O., and Pilegaard K., 2007a. Strong low-pass filtering effects on water vapour flux measurements with closed-path eddy correlation systems. Agr. Forest Meteorol., 147: 140-156.

Ibrom A., Dellwik E., Larsen S.E., and Pilegaard K., 2007b. On the use of the Webb - Pearman - Leuning - theory for closed-path eddy correlation measurements. Tellus B, 59B: 937-946.

Kaimal J.C. and Finnigan J.J., 1994. Atmospheric Boundary Layer Flows. Their Structure and Measurement. Oxford University Press.

Kaimal J.C. and Kristensen L., 1991. Time series tapering for short data samples. Bound-Lay. Meteorol., 57(1-2), 187-194.

Kaimal J.C., Wyngaard J., Izumi Y., and Coté O.R., 1972. Spectral characteristics of surface-layer turbulence (No. AFCRL-72-0492). Air Force Cambridge Research Labs Hanscom Afb Ma.

Kljun N., Calanca P., Rotach M.W., and Schmid H.P., 2004. A simple parameterisation for flux footprint predictions. Bound-Lay. Meteorol., 112(3), 503-523.

Kljun N., Calanca P., Rotach M.V., and Schmid H.P., 2015. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP). Geosci. Model Dev., 8, 3695-3713.

Kormann R. and Meixner F.X., 2001. An analytical footprint model for non-neutral stratification. Bound-Lay. Meteorol., 99(2), 207-224.

Kowalski A.S. and Serrano-Ortiz P., 2007. On the relationship between the eddy covariance, the turbulent flux, and surface exchange for a trace gas such as CO2. Bound-Lay. Meteorol., 124(2), 129-141.

Kroon P.S., Hensen A., Jonker H.J.J., Ouwersloot H.G., Vermeulen A.T., and Bosveld F.C., 2010. Uncertainties in eddy covariance flux measurements assessed from CH4 and N2O observations. Agr. Forest Meteorol. 150: 806-816.

Lasslop G., Reichstein M., Papale D., et al., 2010. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol., 16(1), 187-208.

Leclerc M.Y. and Thurtell G.W., 1990. Footprint prediction of scalar fluxes using a Markovian analysis. Bound-Lay. Meteorol., 52(3), 247-258.

Lee X., Massman W., and Law B. (Eds), 2004. Handbook of micrometeorology: a guide for surface flux measurement and analysis (Vol. 29). Kluwer Academic Publishers, Dordrecht, the Netherlands.

Liu H., Peters G., and Foken T., 2001. New equations for sonic temperature variance and buoyancy heat flux with an omnidirectional sonic anemometer. Bound-Lay. Meteorol., 100(3), 459-468.

Mammarella I., Kolari P., Vesala T., and Rinne J., 2007. Determining the contribution of vertical advection to the net ecosystem exchange at Hyytiälä forest, Finland. Tellus 59B, 900-909, DOI: 101111/j.1600-0889.2007.00306.x

Mammarella I., Launiainen S., Grönholm T., Keronen P., Pumpanen J., Rannik Ü., and Vesala T., 2009. Relative humidity effect on the high-frequency attenuation of water vapour flux measured by a closed-path eddy covariance system. J.Atmos. Ocean.Technol., 26: 1856-1866.

Mammarella I., Peltola O., Nordbo A., Järvi L., and Rannik Ü., 2016. Quantifying the uncertainty of eddy covariance fluxes due to the use of different software packages and combinations of processing steps in two contrasting ecosystems, Atmos. Meas. Tech., 9, 4915-4933.

Mauder M., Cuntz M., Drüe C., et al., 2013. A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agr. Forest Meteorol., 169, 122-135.

Massman W.J. and Ibrom A., 2008. Attenuation of concentration fluctuations of water vapour and other trace gases in turbulent tube flow. Atmos. Chem. Phys., 8: 6245-6259.

Massman W.J., 2000. A simple method for estimating frequency response corrections for eddy covariance systems. Agr. Forest Meteorology, 104: 185-198.

Mauder M. and Foken T., 2006. Impact of post-field data processing on eddy covariance flux estimates and energy balance closure. Meteorologische Zeitschrift, 15(6), 597-609.

Mauder M., Oncley S.P., Vogt R., et al., 2007. The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods. Bound-Lay. Meteorol., 123(1), 29-54.

McMillen R.T., 1988. An Eddy-correlation Technique With Extended Applicability to Non-simple Terrain. Bound-Lay. Meteorol., 43: 231-245.

Moncrieff J., Clement R., Finnigan J., and Meyers T., 2004. Averaging, detrending, and filtering of eddy covariance time series. In: Handbook of Micrometeorology. Springer, Netherlands.

Moncrieff J.B., Massheder J.M., De Bruin H., et al., 1997. A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide. J. Hydrol., 188, 589-611.

Moore C.J., 1986. Frequency response corrections for eddy correlation systems. Bound-Lay. Meteorol., 37(1-2), 17-35.

Nakai T. and Shimoyama K., 2012. Ultrasonic anemometer angle of attack errors under turbulent conditions. Agr. Forest Meteorol., 162, 14-26.

Nakai T., Van Der Molen M.K., Gash J.H.C., and Kodama Y., 2006. Correction of sonic anemometer angle of attack errors. Agr. Forest Meteorol., 136(1), 19-30.

Nicolini G., Aubinet M., Feigenwinter C., et al., 2018. Impact of CO2 storage flux sampling uncertainty on net ecosystem exchange measured by eddy covariance. Agr. Forest Meteorol., 248, 228-239.

Nordbo A., Järvi L., and Vesala T., 2012. Revised eddy covariance flux calculation methodologies - effect on urban energy balance. Tellus Ser. B-Chem. Phys. Meteorol., 64: 18184.

Nordbo A. and Katul G., 2013. A wavelet-based correction method for eddy-covariance high-frequency losses in scalar concentration measurements. Bound-Lay. Meteorol., 146(1), 81-102.

Nordbo A., Kekäläinen P., Siivola E., Mammarella I., Timonen J. and Vesala T., 2014. Sorption-caused attenuation and delay of water vapor signals in eddy covariance sampling tubes and filters. J. Atmos. Oceanic Technol., 31, 2629-2649.

Papale D., Reichstein M., Aubinet M., et al., 2006. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences, 3(4), 571-583.

Pasquill F. and Smith F.B., 1983. Atmospheric diffusion.: Study of the dispersion of windborne material from industrial and other sources. Wiley Press, New York, USA.

Rannik Ü., Peltola O., and Mammarella I., 2016. Random uncertainties of flux measurements by the eddy covariance technique, Atmos. Meas. Tech., 9, 5163-5181, doi:10.5194/amt-9-5163-2016.

Rannik Ü. and Vesala T., 1999. Autoregressive filtering versus linear detrending in estimation of fluxes by the eddy covariance method. Bound-Lay. Meteorol., 91: 259-280.

Reichstein M., Falge E., Baldocchi D.D., and Papale D., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol., 11:1-16.

Richardson A.D., Aubinet M., Barr A.G., Hollinger D.Y., Ibrom A., Lasslop G., and Reichstein M., 2012. Uncertainty Quantification. In: Eddy Covariance: A Practical Guide to Measurement and Data Analysis (Eds M. Aubinet, T. Vesala, D. Papale). Springer Atmospheric Sciences, Dordrecht, The Netherlands.

Schmid H.P., 1994. Source areas for scalars and scalar fluxes. Bound-Lay. Meteorol., 67(3), 293-318.

Schotanus P., Nieuwstadt F., and De Bruin H.A.R., 1983. Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Bound-Lay. Meteorol., 26(1), 81-93.

Smith S.W., 1997. The scientist and engineer’s guide to digital signal processing. California Technical Pub., San Diego CA, USA.

Spirig C., Neftel A., Ammann C., et al., 2005. Eddy covariance flux measurements of biogenic VOCs during ECHO 2003 using proton transfer reaction mass spectrometry. Atmos. Chem. Phys., 5(2), 465-481.

Stull R.B., 1988. An Introduction To Boundary Layer Meteorology. Kluwer Academic Publishers.

Taipale R., Ruuskanen T.M., and Rinne J., 2010. Lag time determination in DEC measurements with PTR-MS, Atmos. Meas. Tech., 3, 853-862.

Taylor G.I., 1938. The spectrum of turbulence. Proc. R. Soc. Lon. Ser-A, 164(919), 476-490.

Thomas C. and Foken T., 2002. Re-evaluation of integral turbulence characteristics and their parameterisations. 15th Symp. Boundary Layers and Turbulence. Am. Meteorol. Soc., July 14-19, Wageningen, The Netherlands.

Van der Molen M.K., Gash J.H.C., and Elbers J.A., 2004. Sonic anemometer (co) sine response and flux measurement: II. The effect of introducing an angle of attack dependent calibration. Agr. Forest Meteorol., 122(1), 95-109.

Van Dijk A., Moene A.F. and De Bruin H.A.R., 2004. The principles of surface flux physics: theory, practice and description of the ECPACK library. University of Wageningen, Wageningen.

Vickers D. and Mahrt L., 1997. Quality control and flux sampling problems for tower and aircraft data. J. Atmos. Ocean Tech., 14: 512-526.

Webb E.K., Pearman G.I., and Leuning R., 1980. Correction of flux measurements for density effects due to heat and water vapour transfer. Q. J. Roy. Meteor. Soc., 106(447), 85-100.

Wilczak J.M., Oncley S.P., and Stage S.A., 2001. Sonic anemometer tilt correction algorithms. Bound.-Lay. Meteorol., 99, 127-150.

Wilson J.D. and Swaters G.E., 1991. The source area influencing a measurement in the planetary boundary layer: The “footprint” and the “distribution of contact distance”. Bound-Lay. Meteorol., 55(1-2), 25-46.

International Agrophysics

The Journal of Institute of Agrophysics of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2017: 1.242
5-year IMPACT FACTOR: 1.267

CiteScore 2017: 1.38

SCImago Journal Rank (SJR) 2017: 0.435
Source Normalized Impact per Paper (SNIP) 2017: 0.849


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 467 467 123
PDF Downloads 220 220 25