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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 11, 1501-1514, 2018
https://doi.org/10.5194/amt-11-1501-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
15 Mar 2018
Adaptive selection of diurnal minimum variation: a statistical strategy to obtain representative atmospheric CO2 data and its application to European elevated mountain stations
Ye Yuan1, Ludwig Ries2, Hannes Petermeier3, Martin Steinbacher4, Angel J. Gómez-Peláez5,a, Markus C. Leuenberger6, Marcus Schumacher7, Thomas Trickl8, Cedric Couret2, Frank Meinhardt9, and Annette Menzel1,10 1Department of Ecology and Ecosystem Management, Technical University of Munich (TUM), Freising, Germany
2German Environment Agency (UBA), Zugspitze, Germany
3Department of Mathematics, Technical University of Munich (TUM), Freising, Germany
4Empa, Laboratory for Air Pollution/Environmental Technology, Dübendorf, Switzerland
5Izaña Atmospheric Research Center, Meteorological State Agency of Spain (AEMET), Santa Cruz de Tenerife, Spain
6Climate and Environmental Physics Division, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
7Meteorological Observatory Hohenpeissenberg, Deutscher Wetterdienst (DWD), Hohenpeissenberg, Germany
8Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany
9German Environment Agency (UBA), Schauinsland, Germany
10Institute for Advanced Study, Technical University of Munich (TUM), Garching, Germany
anow at: Meteorological State Agency of Spain (AEMET), Delegation in Asturias, Oviedo, Spain
Abstract. Critical data selection is essential for determining representative baseline levels of atmospheric trace gases even at remote measurement sites. Different data selection techniques have been used around the world, which could potentially lead to reduced compatibility when comparing data from different stations. This paper presents a novel statistical data selection method named adaptive diurnal minimum variation selection (ADVS) based on CO2 diurnal patterns typically occurring at elevated mountain stations. Its capability and applicability were studied on records of atmospheric CO2 observations at six Global Atmosphere Watch stations in Europe, namely, Zugspitze-Schneefernerhaus (Germany), Sonnblick (Austria), Jungfraujoch (Switzerland), Izaña (Spain), Schauinsland (Germany), and Hohenpeissenberg (Germany). Three other frequently applied statistical data selection methods were included for comparison. Among the studied methods, our ADVS method resulted in a lower fraction of data selected as a baseline with lower maxima during winter and higher minima during summer in the selected data. The measured time series were analyzed for long-term trends and seasonality by a seasonal-trend decomposition technique. In contrast to unselected data, mean annual growth rates of all selected datasets were not significantly different among the sites, except for the data recorded at Schauinsland. However, clear differences were found in the annual amplitudes as well as the seasonal time structure. Based on a pairwise analysis of correlations between stations on the seasonal-trend decomposed components by statistical data selection, we conclude that the baseline identified by the ADVS method is a better representation of lower free tropospheric (LFT) conditions than baselines identified by the other methods.
Citation: Yuan, Y., Ries, L., Petermeier, H., Steinbacher, M., Gómez-Peláez, A. J., Leuenberger, M. C., Schumacher, M., Trickl, T., Couret, C., Meinhardt, F., and Menzel, A.: Adaptive selection of diurnal minimum variation: a statistical strategy to obtain representative atmospheric CO2 data and its application to European elevated mountain stations, Atmos. Meas. Tech., 11, 1501-1514, https://doi.org/10.5194/amt-11-1501-2018, 2018.
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Short summary
This paper presents a novel statistical method, ADVS, for baseline selection of representative CO2 data at elevated mountain measurement stations. It provides insights on how data processing techniques are critical for measurements and data analyses. Compared with other statistical methods, our method appears to be a good option as a generalized approach with improved comparability, which is important for research on measurement site characteristics and comparisons between stations.
This paper presents a novel statistical method, ADVS, for baseline selection of representative...
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