Balloon-borne ozonesondes are launched around the world, at many sites at least once a week. These electrochemical instruments record ozone partial pressure in situ at high vertical resolution (100–150 m) from the surface to the middle stratosphere (∼ 30–35 km). An interfaced radiosonde provides the pressure (p), temperature (T) and GPS data necessary to geolocate each measurement, and to convert ozone partial pressure to other quantities. The data quality depends on various factors such as sonde type and manufacturer, the preflight characterisation and post-flight processing see, e.g.. However, when standard operating procedures are followed, the three most commonly used sonde types

Nowadays more than 80 % of the stations launch an electrochemical concentration cell (ECC) sonde . The Brewer–Mast sonde has mostly been used by the early sounding stations with long data records , while the Japanese stations fly a carbon iodine cell sonde .

Differential absorption lidars are laser-based active remote sensing systems that operate mostly during clear-sky nights. Profiles of ozone number density vs. geometric altitude are retrieved between the tropopause and 45–50 km from backscattered signals at two wavelengths . While instrument and retrieval set-up differs from one site to another, the NDACC ozone lidar network can be considered as homogeneous within 2 % between 20 and 35 km. In this altitude range both bias and precision are estimated at ∼ 2 % and worsen to 5–10 % at other altitudes due to, e.g. lower signal-to-noise ratios or the saturation of the detectors . The vertical resolution degrades from 0.3 km around the tropopause to 3–5 km in the upper stratosphere . The retrieval algorithms used at the different sites were extensively intercompared and the profile measurements validated against a mobile lidar reference, ozonesondes and microwave radiometers . Furthermore, comparisons to space-based observations over the range 20–40 km showed biases less than ±5 % and a decadal stability better than ±5 % decade-1 . We use data from 13 stratospheric ozone lidars in the NDACC network. Geographical location, measurement period and number of screened profiles over the analysis period are listed in Table . The screening procedure is outlined in Sect. . Whenever lidar data are converted to non-native profile representations, we do so in this work using the p/T information extracted at the time and location of the lidar measurement from the ERA-Interim reanalysis fields  produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).

We first estimate the drift of the satellite data at each ground station. The comparison time series can contain large gaps and/or outliers; see, e.g. the GOMOS comparisons in Fig.  (top panel). Hence robust techniques are needed to estimate not only the drift but also its uncertainty . To this end we use an iterative Tukey-bisquare reweighted least-squares procedure to fit the daily averaged relative difference time series to a linear regression model Δxij(l)=αj(l)(ti-t0)+βj(l)+eij(l). With Δxij(l) as in Eq. () at time ti and grid level l, and the fit residual eij(l). In this model, the fit parameter αj(l) represents the linear drift of the satellite data relative to the ground-based record j, whereas βj(l) is the bias between both records at reference time t0. Time series with less than 10 data points are not regressed. The significance of the estimated α^(l) is tested using a robust estimate of its standard deviation σ^α(l) proposed by , a slightly modified version of the ordinary least-squares expression. Figure  illustrates three time series with superimposed regression results (left panels, blue line) and the corresponding 95 % confidence intervals for α^(l) (right panels, vertical dashed blue lines).

In a second step, the drift estimates α^j(l) are averaged over various ground stations j={1,…,N}. None of the satellite records exhibit a clear latitudinal structure of drift (see, for instance HALOE and Aura MLS at 25 km in Fig. ). Therefore, we average the results over the entire sonde network and over the entire lidar network. Since there is a clear variability in the regression uncertainty across the network, each station estimate is weighted by the inverse of its variance wj(l)=σ^α,j-2(l). The network-averaged drift α¯(l)=∑jwj(l)α^j(l)∑jwj(l), has a standard deviation σα¯(l)=1/∑jwj(l).

The single-site drift uncertainties alone do not always explain the observed variability of the drift estimates over the network. When the number of stations is large enough (N≳20) the distribution of normalised residuals νj(l)=(α^j-α¯)/σ^α,j should have unit variance for realistic estimates σ^α of the variance of α^. That is typically not the case for the dense samplers, that tend to have larger variance as illustrated, for instance, for Aura MLS in Fig.  (right). This suggests an unaccounted-for source of uncertainty, likely related to differences in sampling or inhomogeneities across the ground-based network. We follow an ad hoc approach to incorporate this unknown component, by scaling the uncertainty up σα¯∗(l)=κ(l)×σα¯(l) so that the reduced χ2(l)=1N-1∑jνj(l)2 becomes unity. We also assume, conservatively, that the original regression uncertainty does not overestimate the true uncertainty; hence κ(l)=maxχ2(l),1. In the following, this adjusted standard deviation σα¯∗(l) is used to test the significance of the drift averages at the 5 % level. Figure S2 shows the κ-adjustment factor for each satellite record.

The importance of correct single station uncertainties σ^α,j(l) is evident for the calculation of both the weighted mean and its uncertainty. The possible presence of data gaps, outliers and auto-correlation in the time series led us to cross-check the analytic expression of with a bootstrapping technique . Each comparison time series was resampled 2500 times by replacement of single data points, and subsequently regressed to reconstruct the distribution of α^j(l) (Fig. , right). The 2.5 and 97.5 % quantiles define the 95 % confidence interval (light red area) which is in good agreement with the analytic expression (vertical dashed blue lines). Replacing the analytic by the bootstrap-derived uncertainties in Eqs. () and () changes α¯ and σα¯∗ typically by less than ∼ 0.5 % decade-1 (Fig. S3). Figure  (left) also illustrates the outcome of other sensitivity checks, such as changing the temporal resolution of the time series prior to regression (from daily to monthly, green curve) or adding a 1-year harmonic component to the regression model (orange curve). Again, the results are very consistent, changing α¯ and σα¯∗ typically by less than ∼ 1 % decade-1 (Fig. S3). These cross-checks demonstrate the robustness of the results to changes in the analysis parameters.

The very long record of SAGE II, spanning 21 years, allows for a detailed analysis of its stability. The smallest 1 σ uncertainty derived at single sites in the ozonesonde and lidar networks is, respectively, 0.8 % decade-1 and 1.6 % decade-1. The average drift uncertainty over the ensemble of stations is ∼ 4 % decade-1. The drift results are furthermore very consistent from one station to another, with a spread of 2–3 % decade-1 at 25 km (Fig. ). The sonde and lidar derived estimates are statistically consistent as well. When aggregated over the entire ground network a significant SAGE II drift should be detectable at the 1–2 % decade-1 level, depending on altitude.

In the middle and upper stratosphere, between 20 and 40 km, the average drift is slightly negative except around ∼ 33 km (Fig. ). The negative drift remains smaller than 1–2 % decade-1 and is not significant. At lower altitudes the drift becomes gradually more pronounced, but is never significant either as a result of the increased atmospheric variability or noise in the SAGE II record. We therefore conclude that the SAGE II record is stable relative to the ground measurements, at least within 2 % decade-1.

SAGE III collected data for only 3.5 years, which excludes the upper stratosphere from our study as no lidar sites provide sufficient statistics. Between 20 and 30 km the minimal drift uncertainty is 6 % decade-1, while that of most stations is easily twice as high.

SAGE III ozone decreases relative to ground measurements, by 2–6 % decade-1 in the middle stratosphere (MS) and more than 10 % decade-1 at lower altitudes (Fig. ). The significance is by far insufficient however for a 2 σ detection. The detection limit for the network-averaged drift is at best 6 % decade-1 between 20 and 30 km. In the lower stratosphere (LS) the threshold rapidly worsens to 10–30 % decade-1 due to the increased contribution of noise from natural variability and instrumental noise. We therefore conclude that SAGE III is stable within about ±10 % decade-1, which is consistent with an earlier report by .

The 14-year HALOE record allows for a quite detailed study of the stability as well. The typical uncertainty at single stations is 5 % decade-1, which is comparable to the variability of the spread between stations (Fig. , top left, light grey band). The 2 σ detection threshold for the network average is 2–3 % decade-1 or more.

For altitudes above 100 hPa we observe a negative drift of about 1–7 % decade-1 (Fig. ). The result is significant between 10 and 40 hPa for both the ozonesonde and the lidar comparisons. Figure  demonstrates that negative drifts are found across the entire ground network (left panel), all centred around the network-averaged value (right). At altitudes above 10 hPa and below 40 hPa the drift is less than ±5 % decade-1 with an uncertainty of 1.5–6 % decade-1 and hence not significant. No dependence on vertical coordinate or ozone quantity was found for the HALOE drift results (Fig. ), so these cannot be explained by drifting auxiliary data of the correlative records (Sect. ).

Two earlier studies concluded that HALOE does not drift significantly relative to SAGE II, at least not more than ±10–15 % decade-1 . Due to the longer data record considered here, the more frequent sampling and the stability of the ground networks we obtain a significant result already at the 2–3 % decade-1 level between 10 and 50 hPa. Our result is consistent with the earlier reports, although a direct comparison is not straightforward due to the different timespan and vertical coordinate (we come back to this in Sect. ). From Fig.  we infer that the middle stratospheric drift of HALOE relative to SAGE II must range between 0 and -5 % decade-1, which is comparable in sign and in magnitude with the -(0–10) % decade-1 reported by Fig. 4a and -(2–4) % decade-1 by Fig. 8.

The UARS MLS record is somewhat short (less than 6 years) which limits the drift study especially at low altitudes and relative to the lidar instruments. Between 5 and 50 hPa (20–35 km) the single station drift uncertainty is 5 % decade-1 at best, but typically twice as large. When the results are averaged over the ground network the 2 σ detection threshold is 4–8 % decade-1. At other altitudes the threshold increases rapidly, by a factor of at least 2.

For altitudes below 10 hPa the ozonesonde comparisons show a non-significant positive drift of 0–3 % decade-1 (Fig. ). The drift relative to lidar, on the other hand, is negative but it is also not well constrained. As a result, the difference between the sonde- and lidar-derived results is not significant. In fact, it is difficult to conclude anything from the lidar results; the results at different sites tend to be somewhat inconsistent, especially at altitudes above the 10 hPa level. While the upper stratospheric drift of UARS MLS goes up to +10 % decade-1 relative to the Observatoire de Haute-Provence (OHP) and Table Mountain lidars, it goes down to -10 % decade-1 relative to the Mauna Loa and Lauder lidars. This necessitates a large χ2-adjustment of κ≃2.5 for lidar (Eq. () and Fig. S2) and results in a final uncertainty of about 10 % decade-1. We conclude that between 10 and 50 hPa the UARS MLS instrument is stable within about ±5–10 % decade-1, perhaps slightly worse. In the upper stratosphere the discrepancy between the lidar results is too large to assess the stability of UARS MLS.

We also note a dependence of the UARS MLS ozone drift results with profile representation due to an ascending drift in the accompanying GPH profile products (Fig. ). More details and a recommendation to avoid such representation-dependences follow in Sect. .

The stability of the Aura MLS instrument can be studied in great detail, thanks to its excellent temporal and spatial sampling. Single site drift uncertainty is at best 0.6 and 2 % decade-1 on average. Regression uncertainties are substantially smaller than the observed standard deviation of the drifts over the network, which is about 4–6 % decade-1 at altitudes above 50–100 hPa (Fig. , bottom). This leads to a considerable χ2-adjustment (Fig. S2) of κ≃2.5 in the middle stratosphere (sonde) and κ≃3 in the upper stratosphere (lidar). The resulting 2 σ detection limit for network averages is 1–3 % decade-1 at altitudes below 5 hPa, and increases rapidly in the uppermost stratosphere.

In the upper and middle stratosphere the average drift is slightly positive, but generally not more than 1.5–2 % decade-1 (Fig. ). Sonde and lidar derived results are very consistent. A significant negative drift seems to develop at altitudes below 100 hPa, which we think is due to an underestimation of the uncertainty. Indeed, obtaining realistic uncertainties at the level of a few % decade-1 level in the UTLS is a daunting task. We therefore conclude that Aura MLS v3.3 is stable in the entire stratosphere, certainly within 1.5 % decade-1 (MS) and 2 % decade-1 (US). Our ground-based estimates are consistent with earlier intercomparisons of Aura MLS, MIPAS  and OSIRIS , indicating drifts between the instruments less than ±3–5 % decade-1.

We will see later on that the above drift results differ from those in non-native vertical coordinate representations, due to an overall descending drift of the Aura MLS GPH profiles (Fig. ). This issue and a possible solution will be discussed in Sect. .

The analysis of POAM II is extremely limited due to its infrequent sampling and short record, merely 3 years. The regression requirement of at least 10 data points was met at just 7 polar ozonesonde stations. There were not enough co-locations with lidar instruments to study the upper stratosphere. Drift uncertainty is about 30 % decade-1 at most sites and 20 % decade-1 in the best case. The resulting 2 σ detection threshold for the network average is 20–40 % decade-1 in the middle stratosphere. This is much larger than the observed drifts, which range from -15 % decade-1 at 20 km and 30 km to +15 % decade-1 at 25 km (Fig. ). We conclude that the stability of POAM II is better than ±25 % decade-1 in the middle stratosphere.

The POAM III data record spans 7.5 years and can therefore be studied in greater detail than that of its predecessor. In addition to the seven polar stations in the POAM II drift analysis, five ozonesonde sites at northern mid-latitudes provide a sufficiently sampled time series. Again, the regression was not feasible for lidar comparisons, limiting the altitude of our analysis to 30 km. The single station uncertainty is 4 % decade-1 at best and about 6 % decade-1 on average. When the results are averaged, the 2 σ drift uncertainty becomes 4–8 % decade-1 in the middle stratosphere and rapidly grows to 10 % decade-1 at 15 km. Overall, POAM III seems to drift to lower ozone values between 20 and 30 km, at a rate of -(2–8) % decade-1 (Fig. ). At lower altitudes the drift changes sign. None of our results are statistically significant. We conclude that POAM III is stable within, respectively, ±5 and 15 % decade-1 in the middle and lower stratosphere.

The OSIRIS time series are densely sampled at many ground stations. In the middle and upper stratosphere the minimum drift uncertainty is 1.3 % decade-1 and typically amounts to 3–4 % decade-1. The regression uncertainties do not fully explain the observed variability of 5–6 % decade-1 between stations above 20 km. The corresponding χ2-adjustment factor κ is ∼ 1.5–2 for the sonde network and mostly less than 1.5 for the lidar network. The 2 σ detection limit for the network average is 3 % decade-1 at 15 km, 1.6 % decade-1 between 20 and 30 km and 5 % decade-1 at 45 km.

In the lowermost stratosphere the OSIRIS drift relative to correlative measurements is negative, at most -5 % decade-1 and not significant (Fig. ). There are clear indications of a positive drift between 15 and 35 km, of about 1–3 % decade-1. While the sonde-derived result is significant (> 22 km), that is generally not the case for the lidar results (except between 28 and 34 km). In the upper stratosphere the positive drift becomes more pronounced and very significant above 37 km. Its presence is easily visible in the comparison time series, e.g. at the OHP lidar (Fig. ). Around 42 km we find a > 2 σ drift of +8 % decade-1 at three of the four best sampled lidar stations (Fig. ). reported a +(3–6) % decade-1 drift of OSIRIS relative to Aura MLS in the US, depending on how the Aura MLS data (pressure-VMR) are converted to the native OSIRIS system (altitude-number density). This is consistent with the 5 % decade-1 difference that we find between our lidar-based drift estimates for these two instruments. Also obtained positive drift estimates of OSIRIS relative to five satellite instruments above 40 km, though the results are not significant for most instrument pairs.

In summary, OSIRIS ozone drifts very likely to higher values above 20 km. The drift is quite small up to 35 km and close to the 5 % significance threshold. In the upper stratosphere the presence of a +(5–8) % decade-1 drift is evident. The OSIRIS team has found that the drift in ozone may be caused by a positive drift in the altitude registration. Efforts are under way to correct for this in the next data release.

Even though the SMR record spans 12 years and has good sampling properties, the ability to assess its stability is limited by the noise of the profiles. In Sect.  we show that the single SMR profile noise exceeds 20 % in the tropics and 30 % at higher latitudes. This is substantially larger than for any other satellite record in this study. As a result, the drift uncertainty is at best 5–6 % decade-1 and typically ∼ 10 % decade-1 at individual ground sites. The regression uncertainties cover the observed drift variability across the ground network, so the χ2-adjustment is close to one. In the end, the 2 σ threshold to detect averaged drifts ranges from 3 to 10 % decade-1 between 25 and 40 km.

The SMR profile drifts slightly to higher values in the middle stratosphere, although by no more than +5 % decade-1 which is insignificant (Fig. ). Above 30 km the drift changes sign and increases rapidly in magnitude, reaching -12 % decade-1 around 40 km. Due to the large single-profile noise the negative drift is only significant at 2 σ level between 40 and 43 km. A recent six-satellite intercomparison study pointed to a negative drift of SMR upper stratospheric ozone as well, though the estimates were generally not considered significant . These results contrasts with satellite intercomparisons by which indicated an insignificant positive drift of SMR relative to a multi-satellite average in the upper stratosphere. The difference may be due to a shorter period (2001–2007) or due to the different data versions, and deserves further study. Meanwhile, we conclude that SMR is stable within ±6–8 % decade-1 over most of the stratosphere. SMR ozone trends in the uppermost stratosphere, however, should be interpreted cautiously as they possibly underestimate the actual trend by more than 10 % decade-1.

The constraints on the stability of GOMOS are weaker than for its contemporary limb sounders, due to its sparser sampling and, below ∼ 20 km, its larger noise. These limitations are clear from the comparison time series at the Payerne ozonesonde station (Fig. ). In the middle stratosphere, the drift variability between stations is about 10 % decade-1, which is larger than the uncertainties at individual sites, about 3 % decade-1 at best and 7 % decade-1 in general. The χ2-adjustment increases the uncertainty of the network averages by κ≃1.5. The resulting 2 σ detection threshold is 3–5 % decade-1 between 20 and 40 km and raises rapidly in the lower stratosphere, e.g. to ∼ 12 % decade-1 at 15 km.

In the upper stratosphere the lidar results are scattered, but they point on average to a small, positive drift of GOMOS retrievals above 35 km (Fig. ). The maximum drift is only +3 % decade-1 at 45 km, well below the 2 σ threshold. However, below 25–30 km a pronounced negative drift develops with decreasing altitude, from -1 % decade-1 at 30 km to -4 % decade-1 at 20 km (Fig. ). The results for the ozonesonde and lidar networks are qualitatively and quantitatively consistent, the latter being less significant below 22 km. GOMOS drift estimates are close to the 2 σ threshold between 15 and 25 km. At lower altitudes, the significance decreases due to markedly increased noise. Various other studies corroborate our observation of a negative drift in the lower stratosphere. reported a drift of up to -18±8 % decade-1 (1 σ uncertainty) near 20 km relative to the OHP lidar (43.9∘ N, 5.7∘ E). Similarly, intercomparisons pointed to a negative drift of GOMOS lower stratospheric ozone relative to all of its contemporary limb sounders .

We only consider profiles from 2005–2012 (optimised resolution period, OR) in the nominal observation mode, since other MIPAS data is less recommended for use in long-term studies. Nevertheless, the stability can still be studied down to several % decade-1 thanks to the good sampling properties of the instrument. In the middle and upper stratosphere, the smallest single-site regression uncertainty is 1.5 % decade-1 and typically ∼ 3 % decade-1. These errors do not fully cover the observed variability between sonde stations. They are therefore scaled by a factor of κ≃2 between 3 and 50 hPa and ≃1 at altitudes below 100 hPa. The resulting 2 σ detection limit for the network average is 2–4 % decade-1 between 10 and 100 hPa and 5–9 % decade-1 in the upper stratosphere.

No significant drift is observed in the MIPAS OR profiles, they are stable relative to the ground-based networks over the entire considered altitude range. Drift estimates are less than ±2 % decade-1 in the middle and upper stratosphere, and less than ±4 % decade-1 at lower altitudes (Fig. ). , on the other hand, noted clear negative drifts in the upper stratosphere between MIPAS data retrieved by the Level-2 processor at Karlsruhe Institute of Technology and Aura MLS (0.2–0.3 ppmv decade-1, or ∼ 3–5 % decade-1) or OSIRIS (0.3–0.6 ppmv decade-1, or ∼ 5–10 % decade-1). The seemingly contrasting results from both analyses are nevertheless in good agreement. We deduce from the lidar-based drift estimates that the relative drift between MIPAS and Aura MLS or OSIRIS would be, respectively, -(2–5) and -(3–10) % decade-1 for altitudes above 5 hPa (Fig. ).

Our drift results are generally not applicable for trend analyses which include MIPAS data prior to 2005 (full resolution period, FR). The FR data are biased relative to the OR profiles , which will introduce an (altitude-dependent) systematic uncertainty in trend analyses if not accounted for. overcome this issue by including the FR-OR bias as a free parameter in the regression model.

The excellent sampling of SCIAMACHY allows us to probe its stability down to 0.8 % decade-1 at some ground sites, and on average down to ∼ 2 % decade-1. Again, these statistical uncertainties do not cover the variability of 6 % decade-1 observed between the stations, leading to a κ≃2-2.5 adjustment over most of the middle stratosphere. The drift averages become significant when they cross the 2–6 % decade-1 bar in the middle and upper stratosphere.

SCIAMACHY data below 30 km drift to higher values relative to sondes and lidars (Fig. ). The drift is nearly independent of altitude and amounts to about +2 % decade-1. The sonde results surpass the 2 σ threshold, but those derived from lidar observations do not. The drift has the opposite sign above 30 km and becomes rapidly highly significant at all lidar sites (Fig. ). It reaches maximal significance, more than 5 σ, around 38 km with a magnitude of -9 % decade-1. Figure  illustrates the negative drift at 38 km in the comparison time series for the Mauna Loa lidar (19.5∘ N, 155.6∘ W).

These results clearly show that SCIAMACHY trend results should be interpreted very cautiously in the upper stratosphere, and likely at lower altitudes as well. For instance, the large negative drift in SCIAMACHY US ozone explains, at least partially, the more negative trends derived from the IUP Bremen v2.5 data set than those found for Aura MLS and OSIRIS . While latter authors consider a different SCIAMACHY Level-2 processor than us, there have been reports of a negative drift of 5 % decade-1 at 30–40 km for the IUP Bremen processor as well .

The drift in SCIAMACHY data is not well understood and several possible causes are being explored. The SGP 5.02 limb ozone retrieval does not use UV wavelengths, so little information is retrieved in the upper stratosphere and the resulting data will be weighted towards the a priori. Since the latter is taken from an annually repeating climatology, a negative drift in the US can be expected provided that the actual ozone trend is positive in this part of the atmosphere. However, this seems to provide only a partial explanation as the magnitude of the positive trend (about +3–4 % decade-1 between 30 and 40 km) is not nearly as large as the negative drift in SGP 5.02 ozone data (-9 % decade-1). The IUP Bremen data record should be less prone to this effect, since more information is extracted in the US by exploiting the Hartley band. Nonetheless, a negative drift in IUP Bremen data is observed in the US as well, but of smaller magnitude. A second possibility is that the retrieved ozone values change as a result of changes over time in the sensitivity to limb polarisation. The polarisation is currently not well determined but is expected to in a future operational data release (version 7). Meanwhile, further investigations are ongoing.

The solar occultation instruments onboard SCISAT sample mainly high latitudes. We limit our stability study of ACE-FTS to the lower and middle stratosphere, since there is only one lidar site with a sufficient number of co-locations. The best single-site drift uncertainty is 3 % decade-1, whereas it amounts to about 10 % decade-1 in general, close to the observed variability between stations. The observed drift is mostly negative, less than 5 % decade-1, which is consistent with the no-drift hypothesis (Fig. ). The ACE-FTS data record can be considered stable to within about 5 % decade-1. A more precise analysis will be possible once the ACE-FTS profiles taken after September 2010 are included in the analysis.

The uncertainty on the stability of the MAESTRO record is slightly poorer than that of ACE-FTS. The larger single-station uncertainties, at least 5 % decade-1 and typically 12–14 % decade-1, lead to a 2 σ detection threshold at 6–8 % decade-1 and 6–25 % decade-1 in the middle and lower stratosphere, respectively. The results never cross these thresholds: below 20 km we find a drift between -7 and +10 % decade-1, above 20 km the drift is mainly positive and about 2–3 % decade-1 (Fig. ). Hence, the MAESTRO record is considered stable within ±6–10 % decade-1. Again, as for ACE-FTS, the uncertainty will decrease once the post-September 2010 profiles will be added to the analysis.

Between 20 and 40 km SAGE II ozone remains mostly within ±3 % of the correlative measurements. Above 30–35 km, however, sunrise profiles have a ∼ 4 % more negative bias relative to lidar than sunset profiles (Fig. S19). This confirms, qualitatively, earlier reports of 8–10 % smaller sunrise concentrations than at sunset in the middle and upper stratosphere . In the lowermost stratosphere, and below, ozone is underestimated by up to 10–15 %. The spread in the comparisons is lowest between 25 and 40 km and shows poleward increases, 5 % at the Equator and ∼ 10 % at the high latitudes. Below 20 km the observed spread increases rapidly to 20–30 %, and especially under Antarctic ozone hole conditions.

The stratospheric bias of SAGE III is mostly less than ±3 %, comparable to that of its predecessor. Ozone is generally slightly overestimated except in the Arctic between 10 and 35 km and below ∼ 15 km at mid latitudes. The latter contrasts with a high bias up to 10 % seen at 13 km by for an earlier version of the data set. It is not clear whether the SAGE III sunrise and sunset profiles are biased relative to each other. Figure S19 shows that the bias relative to lidar is ∼5 % more positive for sunrise measurements above 30 km. However, it is not possible to attribute this to diurnal variation since the type of occultation depends on the hemisphere (sunset in North, sunrise in South) and there may be a meridian structure in the instrument bias field (Fig. S6). The short-term variability seems a few percent better than that of SAGE II, i.e. about 5 % at mid-latitudes and 8 % in the Arctic. Below 20 km and above 35–40 km the variability in the comparisons increases markedly.

In the upper stratosphere and tropical middle stratosphere HALOE overestimates ozone by up to 3 %. In contrast, a negative bias is noted over the rest of the atmosphere. In the middle stratosphere it is not more than 5 % but it decreases rapidly at altitudes below 50 hPa, reaching at least 25 % at 200 hPa. The variability in the comparisons is similar to that from the SAGE instruments, ranging from 5–10 % in the middle and upper stratosphere, and peaking at 30–40 % around the tropopause. During the Antarctic ozone hole season, the volume mixing ratios are overestimated by 25 % and the spread increases to 35 %. Our results are consistent with earlier satellite and ground-based studies . reported a 2–5 % positive bias of sunset relative to sunrise occultations above 40 km. The lidar-based analysis seems to confirm this, differences between both occultation types are less than 2 % below 40 km and somewhat higher in the uppermost stratosphere (Fig. S19).

Our findings corroborate most of those by : (a) at altitudes above 50 hPa UARS MLS slightly overestimates ozone by up to 5 %, (b) the 68 hPa and 100 hPa levels exhibit larger biases up to 10 %, and (c) the bias peaks at 68 hPa. However, negative biases up to 5 % are seen between 10 and 50 hPa relative to southern ozonesondes and at altitudes above 5 hPa relative to northern lidars. The short-term variability is similar to the previous records, but reaches the 5–10 % range somewhat higher up in the middle stratosphere, around 20 hPa. At lower altitudes the comparison spread increases fast, maximizing at more than 40–50 % at the tropopause. We noted furthermore that the UARS MLS bias depends on the profile representation if one uses the GPH and temperature data included in the MLS product to perform conversions. This will be discussed in more detail in Sect. .

Aura MLS ozone remains within ±3 % of correlative measurements between 5 and 50 hPa, except in the Arctic where a negative bias of 5 % is noted. The most striking bias characteristics are the stationary vertical oscillations found in the finer vertical retrieval grid results (version 3.3/3.4 data, see ). They are very pronounced in the tropical UTLS where the amplitude reaches 10–15 %, but also extend to higher latitudes and altitudes, with amplitudes of 3–5 %. The previous data release, v2.2, has a coarser grid in the UTLS and displays fewer oscillations. The recent new release of Aura MLS data (version 4.2) mitigates these oscillations to some extent . The comparison spread shows that the single-profile precision is better than 4–7 % in the middle and upper stratosphere, and starts to degrade for altitudes below 50 hPa. Furthermore, the Aura MLS bias depends on the profile representation if one uses the GPH and temperature data included in the MLS product to perform conversions. We come back to this in Sect. .

We observe a negative bias of about 5–10 % between 20 and 30 km, which becomes rapidly more pronounced at lower altitudes in the Antarctic. This is consistent with earlier satellite and ground-based studies . In the northern lower stratosphere, however, the negative remains less than 5 %. Above 30 km, there is a positive bias of 5–10 % or more relative to the polar lidars. The small lidar comparison sample did not allow us to study sunrise vs. sunset results. As for SAGE III, the observed differences (Fig. S19) could also be due to a meridian dependence of the instrument bias since the occultation type changes with hemisphere. The comparison spread is 5–10 % in the middle and upper stratosphere, and increases below 20 km.

The POAM III bias is less than 5 % in the middle stratosphere and upper stratosphere, and has a negative sign between 18 and 30 km and positive elsewhere. Here, the spread in the comparisons is also similar to its predecessor, ranging between 5 and 10 %. In the lower stratosphere there is an overestimation of at least 10 %, and, again, the spread is more pronounced. Our results corroborate the findings of . Unfortunately, the small comparison sample does not allow us to verify their report of a negative bias of up to 5 % of MS and US sunrise data (taken in the Northern Hemisphere) relative to sunset profiles (in the SH).

Our ground-based bias results are very consistent with those of satellite intercomparisons . OSIRIS ozone remains mostly within ±4 % of correlative measurements above 20 km, but two features stand out. First and foremost, a marked peak in bias around 22 km is seen at all latitudes which is possibly related to biases in the aerosol retrieval preceding the ozone retrieval . The comparison to lidars in the tropics and the Southern Hemisphere shows a second jump towards a persistent 5 % positive bias, occurring between 30 and 35 km. Such a feature is not seen in the Northern Hemisphere. In the lower stratosphere, below 20 km, OSIRIS underestimates ozone by 5–10 % at mid and high latitudes and by more than 15 % in the tropics. Comparison spreads range from 6 to 11 % between 20 and 35–40 km. In the UTLS these increase to 20–40 % at 15 km, depending on latitude.

Our analysis confirms earlier reports by, e.g. of a systematic underestimation by 5–10 % in the upper and (most of the) middle stratosphere. The bias changes sign at lower altitudes and peaks at +5 to +10 % around 20 km. The most notable characteristic is the high comparison spread. It increases in the middle stratosphere from 20 % to 30 % between the tropics and the polar regions, and becomes even larger at other altitudes (Fig. ). The poor single-profile precision is caused by the low signal-to-noise ratio for the 501.8 GHz line used for the ozone retrievals. Better precision can be obtained by averaging the profiles in the logaritmic VMR domain . Alternatively, one could use the SMR ozone products from the stronger 544.6 GHz band; these are clearly less noisy though exhibit larger biases .

The GOMOS ozone bias above 20 km is generally less than ±3 %. The exception is the Arctic where a -7 % bias is found relative to ozonesonde and lidar at 25 km and again at 40 km. This is in agreement with earlier analyses by . Another notable feature is that the sign of the bias in the extratropical UTLS is opposite in both hemispheres. It reaches -20 % in the North and +20 % in the South at 10 km. The larger biases below 20 km are due to the interference of ozone and aerosol retrievals with aerosol models . In the middle and upper stratosphere the comparison spread ranges from 6 % in the tropics to 11 % at high latitudes (Fig. ). Below 20–25 km, GOMOS data becomes notably more noisy; at 15 km the observed spreads amount to 25–50 %, as a result of the increasing opacity of the atmosphere. Theoretically it is expected that profile quality depends on star properties such as magnitude and temperature. However, our analysis confirms (not shown here) an earlier claim by that this is not the case when the recommended screening procedure is applied. The illumination condition of the occultation is clearly a more determining factor, with dark limb profiles offering best data quality.

Due to changes in instrument and retrieval set-up there is an altitude-dependent bias between the first (2002–2004) and later years of the mission of up to 5 % . Our analysis covers the 2005–2012 period only and corroborates earlier findings for the operational and several alternative MIPAS Level-2 processors e.g.. MIPAS OR profiles overestimates ozone systematically over most of the stratosphere, except in the Arctic. At mid and low latitudes there are two bias peaks of +(5–10) % around 50 hPa and 5 hPa. At other pressure levels the bias remains below 5 %. At the bottom of the profile, for p>200 hPa, ozone is overestimated by at least 20 %. In the tropics, the bias briefly flips sign between 50 and 200 hPa, where a very negative bias is found. Between 2 and 50 hPa, the observed spread ranges from 4 % in the tropics to 8 % at higher latitudes. Again, in the UTLS a sharp increase is observed (Fig. ). We also noted a dependence of the MIPAS bias on ozone quantity representation when the pressure and temperature data retrieved by the operational ML2PP 6.0 processor are used to perform conversions. More details follow in Sect. .

The SCIAMACHY bias is clearly positive over most of the atmosphere and manifests an intricate structure in altitude, latitude and season. The agreement with ozonesonde and lidar is better than 10 %, and best at northern mid-latitudes (between 0 and +5 % over 15–40 km). However, the bias easily reaches +10–15 % over a large part of the stratosphere, stretching from 30∘ N–60∘ S (> 25 km) to 60–90∘ S (> 30 km). Similar results were obtained by for an alternative Level-2 processor developed by IUP Bremen. Arctic profile data quality is particularly peculiar (Fig. S20). There is a clear vertical dependence of the bias, peaking at +10 % around 20 km and -10 % at 15 and 30 km. Also, both bias and comparison spread vary strongly with season. The bias at 20 km reaches a maximum of +25 % during boreal winter and a minimum of 0 % in summer. Similarly, the mean comparison spread is about 20 %, but it peaks at 30 % in winter and shrinks to 10 % in summer. At other latitudes the observed spread is never below ∼ 10 %. Furthermore, in Sect.  we will show that the SCIAMACHY bias depends on the profile representation if the pressure and temperature data included in the SCIAMACHY product are used to perform conversions.

ACE-FTS ozone remains generally within about ±3 % from ground-based measurements over the entire stratosphere. The bias is negative relative to Arctic ozonesondes, everywhere else ozone mixing ratios are overestimated. Above 30 km, the comparisons to mid-northern and high-southern lidars indicate a slightly larger positive bias, but not more than 5 %. The relative bias only exceeds 5 % a few km above the tropopause. These observations are in line with other studies . recently reported sunrise-sunset biases in the upper stratosphere of 2–5 % above 40 km. Figure S19 shows differences of similar magnitude between the lidar bias results for both occultation types, but with the opposite sign and penetrating deep into the middle and lower stratosphere. These results are clearly due to statistical fluctuations in the small co-location sample. The ACE-FTS record performs also well in terms of short-term variability. The comparison spread is at most 7 % (10 %) at high latitudes in the middle (upper) stratosphere. It increases strongly below 20 km.

The MAESTRO profiles exhibit typically a negative bias in the Northern Hemisphere (3 to 6 %) and a positive bias in the Southern Hemisphere (0 to 10 %). Ozone is clearly underestimated below 15 km, by at least 20 % at all latitudes. These findings confirm those by other authors . Earlier reports of a negative bias of up to 20 % between sunrise and sunset measurements above 35 km can not be confirmed or excluded. Our lidar-based bias results for sunset and sunrise data differ less than 5 % in the upper stratosphere (Fig. S19). Nonetheless, the comparison sample is quite small so it does not necessarily provide a representative picture. The observed comparison spreads range from 7 % at mid-latitudes to 10 % in the Arctic.