Abstract. Six collocated spectrophotometers based in Arosa/Davos, Switzerland, have been measuring ozone profiles continuously since 1956 for the oldest Dobson and since 2005 for the most recent Brewer instrument. The datasets of these 2 ground-based triads (3 Dobsons and 3 Brewers) allow continuous intercomparisons and derivation of long-term trend estimates. In this study, the post-2000 ozone profile trends are estimated from the Dobson and the Brewer Umkehr time series, following a careful homogenization of the Dobson D051 dataset. Mainly, two periods in the post-2000 Dobson D051 dataset show anomalies when compared to the Brewer triad time series: in 2011–2013, an offset has been attributed to technical interventions during the renewal of the spectrophotometer acquisition system; in 2018, an offset with respect to the Brewer triad and Aura MLS has been detected following a technical intervention on the spectrophotometer wedge. The Dobson D051 time series was homogenized using its difference to the Brewers triad N values time series and the ozone profiles were retrieved from the modified N values by optimal estimation method (OEM). Simultaneously, the Dobson D051 time series was optimized by NOAA relying on the NASA M2GMI model simulations to identify and correct instrumental artifacts in the record. Comparisons of the two homogenized data records show common correction periods and globally similar intensities of the N values corrections. Long-term trends were estimated by Dynamic Linear Modeling (DLM) from Dobson ozone profile time series and, for the first time, from Brewer ozone profile time series. A positive trend of 0.2 to 0.5 %/year is estimated above 35 km, significant for Dobson D051 but lower and therefore non significantly different from zero at the 95 % level of confidence for Brewer B040. As shown on the Dobson D051 data record, the trend seems to become significantly positive only in 2004. Moreover, a persistent negative trend is estimated in the middle and the lower stratosphere with different levels of significance depending on the dataset.;
