We present an original multi-spectrum fitting procedure to retrieve volume mixing ratio (VMR) profiles of carbonyl fluoride (COF2) from ground-based high resolution Fourier transform infrared (FTIR) solar spectra. The multi-spectrum approach consists of simultaneously combining, during the retrievals, all spectra recorded consecutively during the same day and with the same resolution. Solar observations analyzed in this study with the SFIT-2 v3.91 fitting algorithm correspond to more than 2900 spectra recorded between January 2000 and December 2007 at high zenith angles, with a Fourier Transform Spectrometer operated at the high-altitude International Scientific Station of the Jungfraujoch (ISSJ, 46.5° N latitude, 8.0° E longitude, 3580m altitude), Switzerland. The goal of the retrieval strategy described here is to provide information about the vertical distribution of carbonyl fluoride. The microwindows used are located in the υ1 or in the υ4 COF2 infrared (IR) absorption bands. Averaging kernel and eigenvector analysis indicates that our FTIR retrieval is sensitive to COF2 inversion between 17 and 30 km, with the major contributi ... mehron to the retrieved information always coming from the measurement. Moreover, there was no significant bias between COF2 partial columns, total columns or VMR profiles retrieved from the two bands. For each wavenumber region, a complete error budget including all identified sources has been carefully established. In addition, comparisons of FTIR COF2 17–30 km partial columns with KASIMA and SLIMCAT 3-D CTMs are also presented. If we do not notice any significant bias between FTIR and SLIMCAT time series, KASIMA COF2 17–30 km partial columns are lower of around 25%, probably due to incorrect lower boundary conditions. For each times series, linear trend estimation for the 2000–2007 time period as well as a seasonal variation study are also performed and critically discussed. For FTIR and KASIMA time series, very low COF2 growth rates (0.4±0.2%/year and 0.3±0.2%/year, respectively) have been derived. However, the SLIMCAT data set gives a slight negative trend (−0.5±0.2%/year), probably ascribable to discontinuities in the meteorological data used by this model. We further demonstrate that all time series are able to reproduce the COF2 seasonal cycle, which main seasonal characteristics deduced from each data set agree quite well.