ICOS eddy covariance flux-station site setup: a review

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The Integrated Carbon Observation System Research Infrastructure aims to provide long-term, continuous observations of sources and sinks of greenhouse gases such as carbon dioxide, methane, nitrous oxide, and water vapour. At ICOS ecosystem stations, the principal technique for measurements of ecosystem-atmosphere exchange of GHGs is the eddy-covariance technique. The establishment and setup of an eddy-covariance tower have to be carefully reasoned to ensure high quality flux measurements being representative of the investigated ecosystem and comparable to measurements at other stations. To fulfill the requirements needed for flux determination with the eddy-covariance technique, variations in GHG concentrations have to be measured at high frequency, simultaneously with the wind velocity, in order to fully capture turbulent fluctuations. This requires the use of high-frequency gas analysers and ultrasonic anemometers. In addition, to analyse flux data with respect to environmental conditions but also to enable corrections in the post-processing procedures, it is necessary to measure additional abiotic variables in close vicinity to the flux measurements. Here we describe the standards the ICOS ecosystem station network has adopted for GHG flux measurements with respect to the setup of instrumentation on towers to maximize measurement precision and accuracy while allowing for flexibility in order to observe specific ecosystem features.

Arriga N., Rannik Ü., Aubinet M., Carrara A., Vesala T., and Papale D., 2017. Experimental validation of footprint models for eddy covariance CO2 flux measurements above grassland by means of natural and artificial tracers. Agric. For. Meteorol. 242: 75-84. doi:https://doi.org/10.1016/j.agrformet.2017.04.006.

Aubinet M., Berbigier P., Bernhofer C.H., Cescatti A., Feigenwinter C., Granier A., et al., 2005. Comparing CO2 storage and advection conditions at night at different CarboEuroflux sites. Boundary-Layer Meteorology 116: 63-94.

Aubinet M., Grelle A., Ibrom A., Rannik Ü., Moncrieff J., Foken T., et al., 2000. Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Adv. Ecol. Res., 30. doi:10.1016/s0065-2504(08)60018-5.

Aubinet M., Feigenwinter C., Heinesch B., Bernhofer C., Canepa E., Lindroth A., et al., 2010. Direct advection measurements do not help to solve the night-time CO2 closure problem: Evidence from three different forests. Agric. For. Meteorol. 150: 655-664. doi:http://dx.doi.

Aubinet M., Joly L., Loustau D., De Ligne A., Chopin H., Cousin J., et al., 2016. Dimensioning IRGA gas sampling systems: laboratory and field experiments. Atmos. Meas. Tech., 9, 1361-1367. doi:10.5194/amt-9-361-2016.

Aubinet M., Vesala T., and Papale D., 2012. Eddy Covariance: A Practical Guide to Measurement and Data Analysis, Dordrecht.

Baldocchi D., 2014. Measuring fluxes of trace gases and energy between ecosystems and the atmosphere – the state and future of the eddy covariance method. Global Change Biol 20: 3600-3609. doi:10.1111/gcb.12649.

Baldocchi D., Chu H., and Reichstein M., 2017. Inter-annual variability of net and gross ecosystem carbon fluxes: A review. Agric. For. Meteorol., doi: https://doi.org/10.1016/j.agrformet.2017.05.015.

Baldocchi D., Falge E., Gu L.H., Olson R., Hollinger D., Running S., et al., 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc., 82: 2415-2434.

Baldocchi D., Ryu Y., and Keenan T., 2016. Terrestrial Carbon Cycle Variability [version 1; referees: 2 approved]. F1000Research 2016, 5 (F1000 Faculty Rev): 2371 (doi: 10.12688/f1000research.8962.1).

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

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

Belcher S.E., Finnigan J.J., and Harman I.N., 2008. Flows through forest canopies in complex terrain. Ecol. Appl., 18: 1436-1453.

Billesbach D.P., 2011. Estimating uncertainties in individual eddy covariance flux measurements: A comparison of methods and a proposed new method. Agric. For. Meteorol., 151: 394-405, doi:10.1016/j.agrformet. 2010.12.001.

Bureau international des poids et mesures / Joint Committee for Guides in Metrology (BIPM/JCGM), 2008. Evaluation of Measurement Data-Guide to the expression of uncertainty in measurement (GUM). 100.

Bureau international des poids et mesures / Joint Committee for Guides in Metrology (BIPM/JCGM), 2008. International Vocabulary of Metrology – Basic and general concepts and associated terms(VIM). BIPM/JCGM.

Burba G., 2013. Eddy Covariance Method for Scientific, Industrial, Agricultural and Regulatory Applications: A Field Book on Measuring Ecosystem Gas Exchange and Areal Emission Rates, Lincoln, Nebraska.

Burba G., Anderson D., Furtaw M., Eckles R., McDermitt D., and Welles J., 2012a. Gas analyser. US Patent 8, 130,379. Date issued: March 6, 2012..

Burba G., Schmidt A., Scott R.L., Nakai T., Kathilankal J., Fratini G., et al., 2012b. Calculating CO2 and H2O eddy covariance fluxes from an enclosed gas analyzer using an instantaneous mixing ratio. Global Change Biol., 18: 385-399. doi:10.1111/j.1365-2486.2011. 2536.x.

Burba G.G., McDermitt D.K., Anderson D.J., Furtaw M.D. and Eckles R.D., 2010. Novel design of an enclosed CO2/H2O gas analyser for eddy covariance flux measurements. Tellus Ser. B-Chem. Phys. Meteorol., 62: 743-748. doi:10.1111/j.1600-0889.2010.00468.x.

Chasmer L., Kljun N., Hopkinson C., Brown S., Milne T., Giroux K., et al., 2011. Characterizing vegetation structural and topographic characteristics sampled by eddy covariance within two mature aspen stands using lidar and a flux footprint model: Scaling to MODIS. J. Geophys. Res.-Biogeosci. 116, doi: G0202610. 1029/2010jg001567.

Christen A., van Gorsel E., Andretta M., Calanca M., Rotach M., and Vogt R., 2000. Intercomparison of ultrasonic anemometers during the MAP-Riviera project. 9th Conf. Mountain Meteorol., August 7-11, Aspen, CO, USA.

Chu H., Baldocchi D.D., John R., Wolf S., and Reichstein M., 2017. Fluxes all of the time? A primer on the temporal representativeness of FLUXNET. J. Geophysical Res.: Biogeosciences 122: 2016JG003576, doi:10.1002/016JG003576.

Detto M., Montaldo N., Albertson J.D., Mancini M., and Katul G., 2006. Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy. Water Resour. Res., 42, W08419, doi: 10.1029/2005WR004693.

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., 112, D17102, doi:10.1029/2006JD008149.

Eder F., Serafimovich A., and Foken T., 2013. Coherent Structures at a Forest Edge: Properties, Coupling and Impact of Secondary Circulations. Boundary-Layer Meteorol., 148: 285-308, doi:10.1007/s10546-013-9815-0.

Ediger K. and Riensche B.A., 2017. Systems and methods for measuring gas flux. US Patent 9,759,703. Date issued: September 12, 2012.

El-Madany T.S., Griessbaum F., Fratini G., Juang J.Y., Chang S.C., and Klemm O., 2013. Comparison of sonic anemometer performance under foggy conditions. Agric. For. Meteorol., 173: 63-73. doi:10.1016/j.agrformet. 2013.01.005.

Eugster W. and Merbold L., 2015. Eddy covariance for quantifying trace gas fluxes from soils. Soil, 1: 187-205. doi: 10.5194/soil-1-187-2015.

Feigenwinter C., Bernhofer C., Eichelmann U., Heinesch B., Hertel M., Janous D., et al., 2008. Comparison of horizontal and vertical advective CO2 fluxes at three forest sites. Agric. For. Meteorol., 148: 12-24. doi:http://dx.doi.org/10.1016/j.agrformet.2007.08.013.

Feigenwinter C., Molder M., Lindroth A., and Aubinet M., 2010. Spatiotemporal evolution of CO2 concentration, temperature, and wind field during stable nights at the Norunda forest site. Agric. For. Meteorol., 150: 692-701. doi:10.1016/j.agrformet.2009.08.005.

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., 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. Met., 107: 1-48.

Foken T., 2008. Micrometeorology, Springer, Berlin.

Foken T., Aubinet M., and Leuning R., 2012. The eddy covariance method. In: Eddy Covariance: A Practical Guide to Measurement and Data Analysis (Eds M. Aubinet, T. Vesala and D. Papale). Springer, Dordrecht.

Foken T. and Leclerc M.Y., 2004. Methods and limitations in validation of footprint models. Agric. For. Meteorol., 127: 223-234.

Foken T., Leuning R., Oncley S.P., Mauder M., and Aubinet M., 2012. Corrections and data quality. In: Eddy Covariance: A Practical Guide to Measurement and Data Analysis (Eds M. Aubinet, T. Vesala and D. Papale). Springer, Dordrecht.

Foken T. and Wichura B., 1996. Tools for quality assessment of surface-based flux measurements. Agric. For. Meteorol., 78, doi:10.1016/0168-1923(95)02248-1.

Frank J.M., Massman W.J., Swiatek E., Zimmerman H.A., and Ewers B.E., 2016. All Sonic Anemometers Need to Correct for Transducer and Structural Shadowing in Their Velocity Measurements. J. Atmospheric Oceanic Technol., 33: 149-167. doi:10.1175/jtech-d-15-0171.1.

Franz D., et al., 2018. Towards long-term standardised carbon and greenhouse gas observations for monitoring Europe’s terrestrial ecosystems. Int. Agrophys., 32, 439-455.

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. Agric. For. Meteorol., 165: 53-63. doi:10.1016/j.agrformet.2012.05.018.

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. doi:10.5194/bg-11-1037-2014.

Furtaw M., EcklesR.,, Burba G., McDermitt D., and Welles J., 2012a. Gas analyser. US Patent US 8,300,218. Date issued: October 30, 2012.

Furtaw M., Eckles R., Burba G., McDermitt D., and Welles J., 2012b. Gas analyser. US Patent US 8,154,714. Date issued: April 10, 2012.

Gielen B., Op de Beeck M., Loustau D., Ceulemans R., Jordan A., and Papale D., 2017. Integrated carbon observation system (ICOS): An Infrastructure to Monitor the European Greenhouse Gas Balance. In: Terrestrial ecosystem research infrastructures: challenges and opportunities (Ed. A. Chabbi). CRC Press, Boca Raton, FL, USA.

GILL, 2017. HS-50 and HS-100 User manual, 3 Axis Horizontally Symmetric Ultrasonic Anemometers. Lymington, Hampshire, UK.

Göckede M., Foken T., Aubinet M., Aurela M., Banza J., Bernhofer C., et al., 2008. Quality control of CarboEurope flux data – Part I: Coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences, 5: 433-450.

Göckede M., Rebmann C., and Foken T., 2004. A combination of quality assessment tools for eddy covariance measurements with footprint modelling for the characterisation of complex sites. Agric. For. Meteorol., 127: 175-188. doi:http://dx.doi.org/10.1016/j.agrformet.2004.07.012.

Griessbaum F. and Schmidt A., 2009. Advanced tilt correction from flow distortion effects on turbulent CO2 fluxes in complex environments using large eddy simulation. Q. J. R. Meteorol. Soc., 135: 1603-1613. doi:10.1002/qj.472.

Hargrove W.W. and Hoffman F.M., 1999. Using multivariate clustering to characterize ccoregion borders. Computing Sci. Eng., 1: 18-25, doi:doi:10.1109/5992.774837.

Hargrove W.W. and Hoffman F.M., 2004. Potential of multivariate quantitative methods for delineation and visualization of ecoregions. Environ. Manag., 34: S39-S60, doi:10.1007/s00267-003-1084-0.

Haslwanter A., Hammerle A., and WohlfahrtG., 2009. Open-path vs. closed-path eddy covariance measurements of the net ecosystem carbon dioxide and water vapour exchange: A long-term perspective. Agric. For. Meteorol. 149: 291-302.

Haughton N., Abramowitz G., De Kauwe M.G., and Pitman A.J., 2018. Does predictability of fluxes vary between FLUXNET sites? Biogeosciences Discuss. 2018: 1-32. doi:10.5194/bg-2018-179.

Heidbach K., Schmid H.P., and Mauder M., 2017. Experimental evaluation of flux footprint models. Agric. For. Meteorol. 246: 142-153. doi: https://doi.org/10.1016/j.agrformet.2017.06.008.

Hill T., Chocholek M., and Clement R., 2017. The case for increasing the statistical power of eddy covariance ecosystem studies: why, where and how? Global Change Biol 23: 2154-2165. doi:10.1111/gcb.13547.

Hinckley E.-L.S., Anderson S.P., Baron J.S., Blanken P.D., BonanG.B., Bowman W.D., et al., 2016. Optimizing available network resources to address questions in environmental biogeochemistry. Bioscience, 66: 317-326, doi:10.1093/biosci/biw005.

Horst T.W. and Lenschow D.H., 2009. Attenuation of scalar fluxes measured with spatially-displaced sensors. Boundary-Layer Meteorol., 130: 275-300, doi:10.1007/s10546-008-9348-0.

Horst T.W. and Weil J.C., 1992. Footprint estimation for scalar flux measurements in the atmospheric surface layer. Bound. Lay. Met., 59: 279-296.

Horst T.W. and Weil J.C., 1994. How far is far enough - the fetch requirements for micrometeorological measurement of surface fluxes. J. Atmospheric Oceanic Technol., 11: 1018-1025. doi:10.1175/1520-0426(1994)011<1018:hfi fet>2.0.co;2.

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

Huq S., De Roo F., Foken T., and Mauder M., 2017. Evaluation of Probe-induced flow distortion of campbell csat3 sonic anemometers by numerical simulation. Boundary-Layer Meteorology 165: 9-28. doi:10.1007/s10546-017-0264-z.

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. Agric. For. 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 Ser. B-Chem. Phys. Meteorol., 59B: 937-946. DOI: 10.1111/j.1600-0889.2007.00311.x.

International Organization for Standardization (ISO), 2007. Meteorology – Air temperature measurements – Test methods for comparing the performance of thermometer shields/screens and defining important characteristics. https://www.iso.org/obp/ui/#iso:std:o:17714:ed-1:v1:en

International Organization for Standardization (ISO), 2009. Quantities and Units - Part 9: Physical Chemistry and Molecular Physics, ISO 80000-9:2009, https://www.iso.org/standard/31894.html

Kaminski T. and Rayner P.J., 2017. Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design. Biogeosciences 14: 4755-4766, doi:10.5194/bg-14-4755-2017.

Keller M., Schimel D.S., Hargrove W., and Hoffman F.M., 2008. A continental strategy for the National Ecological Observatory Network. Front. Ecol. Environ., 6: 282-284. doi:10.1890/1540-9295(2008)6[282:ACSFTN]2.0. CO;2.

Kim J., Guo Q., Baldocchi D.D., Leclerc M., Xu L., and Schmid H.P., 2006. Upscaling fluxes from tower to landscape: Overlaying flux footprints on high-resolution (IKONOS) images of vegetation cover. Agric. For. Meteorol., 136: 132-146.

Kljun N., Calanca P., Rotach M.W., and Schmid H.P., 2004. A simple parameterisation for flux footprint predictions. Boundary-Layer Meteorol., 112: 503-523.

Kljun N., Calanca P., Rotach M.W., and Schmid H.P., 2015. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP). Geosci. Model Dev., 8: 3695-3713. doi:10.5194/gmd-8-3695-2015.

Kljun N., Rotach M.W., and Schmid H.P., 2002. A three-dimensional backward Lagrangian footprint model for a wide range of boundary-layer stratifications. Bound. Lay. Met., 103: 205-226.

Kormann R. and Meixner F.X., 2001. An analytical footprint model for non-neutral stratification. Bound. Lay. Met., 99: 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. Boundary-Layer Meteorol., 124: 129-141.

Kristensen L., Mann J., Oncley S.P., and Wyngaard J.C., 1997. How close is close enough when measuring scalar fluxes with displaced sensors? J. Atmospheric Oceanic Technol., 14: 814-821.

Kröniger K., Banerjee T., De Roo F., and Mauder M., 2017. Flow adjustment inside homogeneous canopies after a leading edge – An analytical approach backed by LES. Agric. For. Meteorol. doi: https://doi.org/10.1016/j.agrformet.2017.09.019.

Lacombe M., Bousri D., Leroy M., and Mezred M., 2011. WMO Field intercomparison of Themometer Screens / Shields and Hygrometers in hot desert conditions. In: Instruments and Observing Methods (Ed. W.M.O.) WMO, Geneva, Switzerland.

Lasslop G., Reichstein M., Kattge J., and Papale D., 2008. Influences of observation errors in eddy flux data on inverse model parameter estimation. Biogeosciences, 5: 1311-1324.

Leclerc M.Y. and Foken T., 2014. Classification of Footprint Models. Springer, Berlin Heidelberg.

Lee X., Massman W., and Law B., 2004. Handbook of Micrometeorology: A Guide for Surface Flux Measurement and Analysis. Kluwer Academic Publisher, Dordrecht.

Le Quéré C., Andrew R.M., Canadell J.G., Sitch S., Korsbakken J.I., Peters G.P., et al., 2016. Global carbon budget 2016. Earth Syst. Sci. Data 8: 605-649. doi:10.5194/essd-8-605-2016.

Leuning R. and Judd M.J., 1996. The relative merits of open-and closed-path analysers for measurement of eddy fluxes. Global Change Biol., 2 (3): 241-253.

LI-COR, 2014. LI-7200 Enclosed CO2/H2O Gas Analyzer Instruction Manual. In: I. LI-COR (editor) Lincoln, Nebraska 68504 USA.

Loescher H.W., Law B.E, Mahrt L., Hollinger D.Y., Campbell J., and Wofsy S.C., 2006. Uncertainties in, and interpretation of, carbon flux estimates using the eddy covariance technique. J. Geophys. Res., [Atmos.] 111.

Mammarella I., Launiainen S., Grönholm T., Keronen P., Pumpanen J., Rannik Ü., et al., 2009. Relative humidity effect on the high-frequency attenuation of water vapor flux measured by a closed-path eddy covariance system. J. Atmospheric Oceanic Technol., 26: 1856-1866. doi:10.1175/2009jtecha1179.1.

Mauder M., Cuntz M., Drüe C., Graf A., Rebmann C., and Schmid H.P., 2013. A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric. For. Meteorol., 169. doi:10.1016/j. agrformet.2012.09.006.

Mauder M., Oncley S.P., Vogt R., Weidinger T., Ribeiro L., Bernhofer C., et al., 2007. The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods. Boundary-Layer Meteorol., 123: 29-54.

Mauder M. and Zeeman M.J., 2018. Field intercomparison of prevailing sonic anemometers. Atmos. Meas. Tech., 11: 249-263. doi:10.5194/amt-11-249-2018.

Metzger S., Burba G., Burns S.P., Blanken P.D., Li J., Luo H., et al., 2016. Optimization of an enclosed gas analyzer sampling system for measuring eddy covariance fluxes of H2O and CO2. Atmos. Meas. Tech., 9: 1341-1359. doi:10.5194/amt-9-1341-2016.

Moore C.J., 1986. Frequency response corrections for eddy correlation systems. Bound. Lay. Met., 37: 17-35.

Munger J.W., Loescher H.W., and Luo H., 2012. Measurement, Tower, and Site Design Considerations. In: Eddy Covariance - A Practical Guide to Measurement and Data Analysis (Eds M. Aubinet, T. Vesala and D. Papale). Springer, Dordrecht.

Nakai T., Iwata H., and Harazono Y., 2011. Importance of mixing ratio for a long-term CO2 flux measurement with a closed-path system. Tellus B: Chemical and Physical Meteorology, 63: 302-308. doi:10.1111/j.1600-0889. 2011.00538.x.

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

Novick K.A., Biederman J.A., Desai A.R., Litvak M.E., Moore D.J.P., Scott R.L., et al., 2017. The AmeriFlux network: A coalition of the willing. Agric. For. Meteorol. doi: https://doi.org/10.1016/j.agrformet.2017.10.009.

Papale D., Black T.A., Carvalhais N., Cescatti A., Chen J., Jung M., et al., 2015. Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks. J. Geophysical Res.: Biogeosci., 120: 1941-1957. doi:10.1002/2015JG002997.

Post H., Vrugt J.A., Fox A., Vereecken H., and Hendricks Franssen H.-J., 2017. Estimation of community land model parameters for an improved assessment of net carbon fluxes at European sites. J. Geophysical Res.: Biogeosci., 122: 661-689. doi:10.1002/2015JG003297.

Rannik Ü., Kolari P., Vesala T., and HariP., 2006. Uncertainties in measurement and modelling of net ecosystem exchange of a forest. Agric. For. Meteorol., 138: 244-257.

Rannik Ü., Sogachev A., Foken T., Göckede M., Kljun N., Leclerc M., et al., 2012. Footprint Analysis. In: Eddy Covariance: A Practical Guide to Measurement and Data Analysis (Eds M. Aubinet, T. Vesala and D. Papale). Springer, Dordrecht.

Raupach M.R., Rayner P.J., Barrett D.J., DeFries R.S., Heimann M., Ojima D.S., et al., 2005. Model-data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications. Global Change Biol., 11: 378-397.

Rebmann C., Kolle O., Heinesch B., Queck R., Ibrom A., and Aubinet M., 2012. Data Acquisition and Flux Calculations. In: Eddy Covariance: A Practical Guide to Measurement and Data Analysis (Eds M. Aubinet, T. Vesala and D. Papale). Springer, Dordrecht.

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

Richardson A.D., Hollinger D.Y., Burba G.G., Davis K.J., Flanagan L.B., Katul G.G., et al., 2006. A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes. Agric. For. Meteorol., 136: 1-18.

Schmid H.P., 1994. Source areas for scalars and scalar fluxes. Bound. Lay. Met., 67: 293-318.

Schmid H.P., 1997. Experimental design for flux measurements: matching the scale of the observations to the scale of the flux. Agric. For. Meteorol., 87: 179-200.

Schmid H.P., 2002. Footprint modeling for vegetation atmosphere exchange studies: a review and perspective. Agric. For. Meteorol., 113: 159-183.

Schmid H.P. and Lloyd C.R., 1999. Spatial representativeness and the location bias of flux footprints over inhomogeneous areas. Agric. For. Meteorol., 93: 195-209.

Schotanus P., Nieuwstadt F.T.M., and De Bruin H.A.R., 1983. Temperature measurements with a sonic anemometer and its application to heat and moisture fluxes. Bound. Lay. Met., 26: 81-93.

Serafimovich A., Thomas C., and Foken T., 2011. Vertical and horizontal transport of energy and matter by coherent motions in a tall spruce canopy. Boundary-Layer Meteorology, 140: 429-451. doi:10.1007/s10546-011-9619-z.

Sogachev A., Leclerc M.Y., Karipot A., Zhang G., and Vesala T., 2005. Effect of clearcuts on footprints and flux measurements above a forest canopy. Agric. For. Meteorol., 133: 182-196.

Sogachev A., Leclerc M.Y., Zhang G., Rannik Ü., and Vesala T., 2008. CO2 fluxes near a forest edge: A numerical study. Ecol. Appl., 18: 1454-1469.

Sogachev A., Rannik Ü., and Vesala T., 2004. Flux footprints over complex terrain covered by heterogeneous forest. Agric. For. Meteorol., 127: 143-158.

Sogachev A. and Sedletski A., 2006. SCADIS “Footprint calculator”: Operating manual. Proc. BACCI, NECC and FCoE activities 2005, (Eds M. Kulmala, A. Lindroth and T.M. Ruuskanen). Aerosolitutkimusseura, Helsinki.

Steinfeld G., Raasch S., and Markkanen T., 2008. Footprints in Homogeneously and Heterogeneously Driven Boundary Layers Derived from a Lagrangian Stochastic Particle Model Embedded into Large-Eddy Simulation. Bound. Lay. Met., 129: 225-248.

Tramontana G., Jung M., Schwalm C.R., Ichii K., Camps-Valls G., Ráduly B., et al., 2016. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences, 13: 4291-4313, doi:10.5194/bg-13-4291-2016.

van Jaarsveld A.S., Pauw J.C., Mundree S., Mecenero S., Coetzee B.W.T., and Alard G.F., 2007. South African Environmental Observation Network: vision, design and status. South African J. Sci., 103: 289-294.

Vesala T., Kljun N., Rannik Ü., Rinne J., Sogachev A., Markkanen T., et al., 2008. Flux and concentration footprint modelling: State of the Art. Environ. Pollut. 152: 653-666. doi:doi:10.1016/j.envpol.2007.06.070.

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

Vogt R. and Feigenwinter C., 2011. Angle of attack performance of five different sonic anemometers in a wind tunnel. Nordflux Workshop on the Accuracy of Eddy Covariance Flux Measurements. Roskilde, Denmark.

Vogt R., Feigenwinter C., Paw U K.T., and Pitacco A., 1997. Intercomparison of ultrasonic anemometers. 12th Symp. Boundary Layers and Turbulence, Vancouver, Canada. Am. Met. Soc., Vancouver, Canada.

Webb E.K., Pearman G.I., and Leuning R., 1980. Correction of the flux measurements for density effects due to heat and water vapour transfer. Quart. J. Roy Meteorol. Soc. 106. doi:10.1002/qj.49710644707.

Welles J.M. and McDermitt D.K., 2005. Measuring carbon dioxide in the atmosphere. Micrometeorology in Agricultural Systems Agron. Monogr., 47, 287-320.

World Meteorological Organization (WMO), 2008. Guide to Meteorological Instruments and Methods of Observation, WMO-No. 8. World Meteorological Organization.

World Meteorological Organization (WMO), 2014. updated in 2017. Guide to Meteorological Instruments and Methods of Observation, WMO-No. 8. World Meteorol. Organization.

Wyngaard J.C., 1981. The Effects of Probe-Induced Flow Distortion on Atmospheric Turbulence Measurements. J. Appl. Meteorol., 20: 784-794.

Wyngaard J.C., 1988. The Effects of Probe-Induced Flow Distortion on Atmospheric Turbulence Measurements: Extension to Scalars. J. Atmos. Sci. 45: 3400-3412. doi:10.1175/1520-0469(1988)045<3400:TEOPIF>2.0. CO;2.

Yamamoto S., Saigusa N., Gamo M., Fujinuma Y., Inoue G., and Hirano T., 2005. Findings through the AsiaFlux network and a view toward the future. J. Geographical Sci., 15: 142-148. doi:10.1007/bf02872679.

Zscheischler J., Mahecha M.D., Avitabile V., Calle L., Carvalhais N., Ciais P., et al., 2017. Reviews and syntheses: An empirical spatiotemporal description of the global surface-atmosphere carbon fluxes: opportunities and data limitations. Biogeosciences, 14: 3685-3703. doi:10.5194/bg-14-3685-2017.

International Agrophysics

The Journal of Institute of Agrophysics of Polish Academy of Sciences

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