Atmospheric CO2 and δ13C measurements from 2012 to 2014 at the environmental research station schneefernerhaus, Germany: Technical corrections, temporal variations and trajectory clustering

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Abstract

This study presents continuous atmospheric CO2 and δ13C measurements by wavelength-scanned cavity ring down spectroscopy (Picarro G1101-i) at the high-mountain station Schneefernerhaus, Germany. δ13C values were post-corrected for methane and water spectral interferences using accompanying measurements of CH4 and H2O, and CO2 in dried air, respectively. The best precision of ±0.2‰ for δ13C and of ±4 ppb for CO2 was obtained with an integration time of about 1 hour for δ13C and 2 hours for CO2. The seasonality of CO2 and δ13C was studied by fitting background curves for a complete 2-year period. Peak-to-peak amplitudes of the averaged seasonal cycle were 15.5 ± 0.15 ppm for CO2 and 1.97 ± 0.53‰ for δ13C, respectively. Based on the HYSPLIT Model, air masses were classified into five clusters, with westerly and northeasterly flows being the most and the least frequent, respectively. In the wintertime, northwest and northeast clusters had a higher median level for ΔCO2 and a lower median level for Δδ13C (the difference between observed and background concentrations), likely caused by anthropogenic emissions. In the summertime, air masses from the northwest had the lowest ΔCO2 and the highest Δδ13C. Potential source contribution functions (PSCFs) were used to identify the potential source and sink areas. In winter, source areas for high CO2 mixing ratios (> 75th percentile) were mainly located in northwestern Europe. In summer, areas with high δ13C ratios (> 75th percentile), indicating a carbon sink, were observed in the air from Eastern and Central Poland.

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Ghasemifard, H., Yuan, Y., Luepke, M., Schunk, C., Chen, J., Ries, L., … Menzel, A. (2019). Atmospheric CO2 and δ13C measurements from 2012 to 2014 at the environmental research station schneefernerhaus, Germany: Technical corrections, temporal variations and trajectory clustering. Aerosol and Air Quality Research, 19(3), 657–670. https://doi.org/10.4209/aaqr.2018.01.0010

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