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

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https://doi.org/10.5194/amt-2017-316
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
12 Sep 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).
Adaptive Baseline Finder, a statistical data selection strategy to identify atmospheric CO2 baseline levels and its application to European elevated mountain stations
Ye Yuan1, Ludwig Ries2, Hannes Petermeier3, Martin Steinbacher4, Angel J. Gómez-Peláez5, Markus C. Leuenberger6, Marcus Schumacher7, Thomas Trickl8, Cedric Couret2, Frank Meinhardt9, and Annette Menzel1,10 1Department of Ecology and Ecosystem Management, Technische Universität München, Freising, Germany
2German Environment Agency (UBA), Zugspitze, Germany
3Department of Mathematics, Technische Universität München, 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 (KIT), Garmisch-Partenkirchen, Germany
9German Environment Agency (UBA), Schauinsland, Germany
10Institute for Advanced Study, Technische Universität München, Garching, Germany
Abstract. Critical data selection is essential for determining representative baseline levels of atmospheric trace gas measurements even at remote measuring sites. Different data selection techniques have been used around the world which could potentially lead to bias when comparing data from different stations. This paper presents a novel statistical data selection method based on CO2 diurnal pattern occurring typically at high elevated mountain stations. Its capability and applicability was studied for atmospheric measuring records of CO2 from 2010 to 2016 at six Global Atmosphere Watch (GAW) 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 implemented for comparison. Among all selection routines, the new method named Adaptive Baseline Finder (ABF) resulted in lower selection percentages with lower maxima during winter and higher minima during summer in the selected data. To investigate long-term trend and seasonality, seasonal decomposition technique STL was applied. Compared with the unselected data, mean annual growth rates of all selected data sets were not significantly different except for Schauinsland. However, clear differences were found in the annual amplitudes as well as for the seasonal time structure. Based on correlation analysis, results by ABF selection showed a better representation of the lower free tropospheric conditions.

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 Baseline Finder, a statistical data selection strategy to identify atmospheric CO2 baseline levels and its application to European elevated mountain stations, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-316, in review, 2017.
Ye Yuan et al.
Ye Yuan et al.

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Short summary
This paper presents a novel statistical data selection method for CO2 measurements at elevated mountain measuring stations. It provides insights on how data processing techniques are critical for measurements and data analyses. By applying different methods on atmospheric CO2 of various mountain stations, our method appears to be a good option as a generalized approach with improved comparability, which is important for researches on station characteristics or data analyses between stations.
This paper presents a novel statistical data selection method for CO2 measurements at elevated...
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