1Belgian Institute for Space Aeronomy, Brussels, Belgium
2Institute for Atmospheric and Climate Science, ETH Zürich, Switzerland
3Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
4Institute of Astrophysics and Geophysics, University of Liège, Liège, Belgium
Abstract. Within the atmospheric research community, there is a strong interest in integrated datasets, combining data from several instrumentations. This integration is complicated by the different characteristics of the datasets, inherent to the measurement techniques. Here we have compared two carbon monoxide time series (1997 till 2007) acquired at the high-Alpine research station Jungfraujoch (3580 m above sea level), with two well-established measurement techniques, namely in situ surface concentration measurements using Non-Dispersive Infrared Absorption technology (NDIR), and ground-based remote sensing measurements using solar absorption Fourier Transform Infrared spectrometry (FTIR). The profile information available in the FTIR signal allowed us to extract an independent layer with a top height of 7.18 km above sea level, appropriate for comparison with our in situ measurements. We show that, even if both techniques are able to measure free troposphere CO concentrations, the datasets exhibit marked differences in their overall trends (−3.21 ± 0.03 ppb year−1 for NDIR vs. −0.8 ± 0.4 ppb year−1 for FTIR). Removing measurements that are polluted by uprising boundary layer air has a strong impact on the NDIR trend (now −2.62 ± 0.03 ppb year−1), but its difference with FTIR remains significant. Using the LAGRANTO trajectory model, we show that both measurement techniques are influenced by different source regions and therefore are likely subject to exhibit significant differences in their overall trend behaviour. However the observation that the NDIR-FTIR trend difference is as significant before as after 2001 is at odds with available emission databases which claim a significant Asian CO increase after 2001 only.