Eddy covariance raw data processing for CO2 and energy fluxes calculation
at ICOS ecosystem stations
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1 | DIBAF, University of Tuscia, via San Camillo de Lellis snc, 01100 Viterbo, Italy |
2 | Institute for Atmosphere and Earth System Research/Physics, PO Box 68, Faculty of Science, FI-00014, University of Helsinki,
Finland |
3 | Research Centre of Excellence Plants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Universiteitsplein 1,
2610, Wilrijk, Belgium |
4 | LI-COR Biosciences Inc., Lincoln, 68504, Nebraska, USA |
5 | Institute of Bio- and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52428, Jülich,
Germany |
6 | Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092 Zürich,
Switzerland |
7 | DTU Environment, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark |
8 | TERRA, Gembloux Agro-Bio-Tech, University of Liège, 5030 Gembloux, Belgium |
9 | Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, KIT Campus Alpin, Kreuzeckbahnstraße 19,
D-82467 Garmisch-Partenkirchen, Germany |
10 | Mazingira Centre, International Livestock Research Institute (ILRI), P.O. Box 30709, 00100, Nairobi, Kenya |
11 | National Ecological Observatory Network, Battelle, 1685 38th Street, CO 80301 Boulder, USA |
12 | Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, 1225 West Dayton Street, Madison,
WI 53706, USA |
13 | Faculty of Science and Technology, Free University of Bolzano, Piazza Università 1, 39100 Bolzano, Italy |
14 | Forest Services, Autonomous Province of Bolzano, Via Brennero 6, 39100 Bolzano, Italy |
15 | Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Via dell’Università 16,
35020 Legnaro, Italy |
16 | Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15, 04318 Leipzig, Germany |
17 | Institute of Atmospheric Physics CAS, Bocni II/1401, CZ-14131 Praha 4, Czech Republic |
18 | Department of Matter and Energy Fluxes, Global Change Research Institute, CAS, Bělidla 986/4a, 603 00 Brno, Czech Republic |
19 | CMCC Euro Mediterranean Centre on Climate Change, IAFES Division, viale Trieste 127, 01100 Viterbo, Italy |
Publish date: 2018-11-19
Int. Agrophys. 2018, 32(4): 495–515
KEYWORDS
ABSTRACT
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.
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