The SAGE II record forms the basis of many long-term ozone datasets (see, e.g., ). As a limb-viewing instrument, the spatial and temporal sampling of SAGE is fairly sparse, with a given latitude measured approximately once per month; however, it is recognized as having good long-term stability and a vertical resolution of ∼ 1 km in the stratosphere, which are characteristics that are likely to be important for analysing the SOR. We use zonal and monthly mean ozone data from October 1984 to August 2005 provided through the WCRP/SPARC Data Initiative (SDI) .

The native retrieval coordinate of SAGE II is units of ozone number densities on altitude levels; data are post-processed to volume mixing ratios (vmr) on pressure levels using temperatures from a meteorological reanalysis dataset. The SAGE II retrieval algorithm was recently updated as part of the version 7.0 release . The SOR in SAGE II v6.2 data has been discussed in a number of studies: e.g. , , and for mixing ratios and for number densities. Here we compare the SOR in the latest v7.0 release to the previous v6.2 in units of number densities and mixing ratios. It is important to conduct this comparison in both sets of coordinates because the temperature record used to convert SAGE II to mixing ratios was changed between v6.2 and v7.0 from National Meteorological Center/National Center for Environmental Prediction (NMC/NCEP) data to Modern Era-Retrospective Analysis for Research and Applications version 1 (MERRA-1) reanalysis data. The impact of this change on the SOR in SAGE II datasets has not been previously evaluated and is described in Sect. .

As a solar occultation instrument, SAGE II profiles can be categorized as a sunrise (SR) or sunset (SS) measurement. There are known variations in the relative number of SR/SS profiles over the SAGE II record. For example, SAGE II obtained profiles in two narrow latitude bands each day, 15 each at sunrise and sunset, but after November 2000 SAGE II measured only one profile per orbit at either SR or SS. These variations in SR/SS sampling have been shown to affect estimates of climatological ozone values due to diurnal cycle effects but could also affect temporal variability in monthly mean ozone values. To account for the possible effects of these sampling issues on the estimation of the SOR, we add an additional term to the multiple linear regression model for SAGE II data that represents the fraction of SR to total (SR + SS) profiles used to generate each monthly mean data point (see Sect. ).

The SAGE II mission stopped measuring in 2005. Since then several satellite instruments have continued to measure ozone, and there are now a number of combined datasets that have extended SAGE II to near the present day. These datasets were recently analysed as part of the WCRP/SPARC SI2N activity to evaluate long-term ozone trends (see , and references therein) and include SWOOSH (Stratospheric Water and OzOne Satellite Homogenized) , GOZCARDS (Global OZone Chemistry And Related trace gas Data records for the Stratosphere) , SAGE–GOMOS (Global Ozone Monitoring by Occultation of Stars) , and SAGE–OSIRIS (Optical Spectrograph and Infrared Imager System) . As mentioned above, SAGE II mixing ratios are produced by the conversion of number densities using an independent temperature record. The uncertainties in the SOR that result from using different stratospheric temperature records for this conversion are demonstrated in Sect. . This leads us to focus our analysis of the SOR on the extended SAGE II records that provide ozone as number densities and are therefore less dependent on the conversion issues that accompany the choice of a particular temperature record (see Sect. ). Since SWOOSH and GOZCARDS currently only provide ozone mixing ratios, we do not analyse them here.

The three extended ozone datasets all include SAGE II v7.0 number densities. Differences in the SOR between the datasets may therefore arise as a result of the more recent measurements used to extend SAGE II and/or from the methods used to merge the different satellite records. Two of the datasets extend SAGE II using GOMOS, which flew on the ENVISAT satellite and covers 2002–2011, but take different approaches for combining the two records. use GOMOS as a reference and adjust SAGE II sunrise and sunset profiles separately at each latitude and altitude; this dataset will be referred to as SAGE–GOMOS 1. Conversely, use SAGE II as a reference and adjust GOMOS data using seasonally varying offsets at each latitude and altitude; this dataset will be referred to as SAGE–GOMOS 2. The third dataset analysed extends SAGE II using OSIRIS data and covers 1984–2013 . Latitude- and altitude-dependent offsets are calculated for the deseasonalized data during the instrument overlap period (January 2002–August 2005), and the OSIRIS data are adjusted to produce a consistent combined SAGE II and OSIRIS time series.

In addition to SAGE II, the other main long-term internally calibrated satellite ozone dataset is comprised of data from the Backscatter Ultraviolet Radiometer (BUV) and Solar Backscatter Ultraviolet Radiometer (SBUV) instruments on board Nimbus satellites and the SBUV/2 instruments on various National Oceanic and Atmospheric Administration (NOAA) satellites. Data are available as mixing ratios on pressure levels from January 1970 to near the present day. As nadir-viewing instruments, the BUV/SBUV records have more frequent global coverage than the limb-viewing SAGE II, but their vertical resolution is at least 1 order of magnitude poorer at pressures greater than ∼ 15 hPa, rendering it more difficult to resolve detailed ozone structures in the mid and lower stratosphere. Since the entire BUV/SBUV record is comprised of multiple records from different satellites, inter-instrument biases and drifts must also be accounted for to produce a homogenized record.

We analyse zonal and monthly mean data from SBUVMOD VN8.0 and the latest release SBUV VN8.6 , thereby complementing previous analyses of the SOR (e.g. ). SBUVMOD VN8.0 covers the period 1970–2009 and was downloaded from http://acd-ext.gsfc.nasa.gov/Data_services/merged/data/sbuv.70-09.za.v8_prof.vmr.rev1.txt.

Two versions of the SBUV VN8.6 record have been produced so far: the SBUVMOD VN8.6 dataset from NASA, which covers 1970–2012 , and the SBUV Merged Cohesive dataset from NOAA, which covers 1978–2012 . These are identical to the datasets analysed as part of the SI2N activity (e.g. ). The two SBUV VN8.6 datasets contain some differences in the data that are included from different instruments within a particular period (see Fig. 1 in ) and in the methods for averaging and merging these data. SBUV Merged Cohesive VN8.6 uses data from a single instrument in any time period; the individual records are then bias-corrected to produce a continuous record . In contrast, SBUVMOD VN8.6 is constructed by averaging all available data within a particular time window . The SBUVMOD datasets extend back to 1970 by including data from the BUV instrument on Nimbus 4 from 1970 to 1976, whereas the SBUV Merged Cohesive dataset starts from 1978 with the first SBUV instrument on Nimbus 7.

Figure  shows time series of monthly and tropical (30∘ S–30∘ N) mean percent ozone anomalies from 1984 to 2004 at select stratospheric levels for SAGE II v6.2 and v7.0 in units of mixing ratios (on pressure surfaces) and number densities (on approximately equivalent altitude surfaces). Data are only plotted where at least half of the points within the tropical band have values in a given month. The lowest panel shows the monthly F10.7 cm solar flux for reference.

The anomalies in the two ozone number density datasets (blue and green lines) are in close agreement in the mid-stratosphere (24, 31, and 36 km) both in terms of high-frequency fluctuations and long-term changes. At 31 km, there are ozone variations that are consistent with a QBO influence. At 36 and 40 km, there are variations that are visibly in phase with the solar cycle, with relatively high ozone values from 1989 to 1992 during the solar cycle 22 maximum and lower ozone values from 1994 to 1998 during the cycle minimum. The data show greater variance in the early and later parts of the records, and fluctuations in phase with the solar cycle are not evident from the time series alone.

The two SAGE II ozone mixing ratio datasets (black and red lines) are also in reasonable agreement for long-term changes in the mid-stratosphere (10 and 30 hPa). However, in the upper stratosphere (1 and 3 hPa), there are substantial differences in both short- and long-term variations. For example, SAGE II v6.2 (black line) shows persistent negative anomalies in the early part of the record which are not evident in v7.0 (red line). These coincide with the 11-year solar cycle 21 minimum from 1985 to 1988. Furthermore, in the latter part of the record, v6.2 shows relatively large-amplitude fluctuations with mean positive anomalies from 2002 to 2004, which coincide with the peak and subsequent declining phase of solar cycle 23. Thus, there are differences in the evolution of ozone between the two SAGE II mixing ratio datasets, particularly in the upper stratosphere. Overall, the two versions of SAGE II number densities are in closer agreement than the mixing ratio data.

Figure a and b show latitude–altitude plots of the annual mean SOR for SAGE II v6.2 and v7.0 number densities, respectively. The 95 % confidence intervals for the SORs in Fig.  expressed as percent ozone anomalies are shown in Fig. . The SORs in Fig. a and b are generally consistent for the two datasets and show positive values of 2–4 % across the tropical and subtropical stratosphere, except for a region of small (< 1 %) negative values at 30 km in the tropics. There is a relative maximum in the SOR of 3–4 % in the tropics at 50 km and two off-equatorial peaks of a similar magnitude at ∼ 40 km and ±35∘. These findings are consistent with and , who found similar 11-year solar-like signals in tropical upper-stratospheric ozone number densities in SAGE II v6.2 and v7.0. The confidence intervals for the SORs in Fig. a and b show the largest uncertainty at the equator at ∼ 45 km, which is close to a maximum in the SOR. The uncertainties between 35 and 45 km are slightly larger in the northern subtropics compared to the southern subtropics. The uncertainties in the lower stratosphere between 22 and 28 km are smaller in magnitude, but this is partly because the SOR is also smaller here (note the confidence intervals are expressed as percent ozone to be directly comparable to Fig. ). Overall, the 95 % confidence intervals are around 30–50 % of the magnitude of the “best-estimate” SOR in Fig. a and b, indicating that there are considerable uncertainties in the SOR in the SAGE II number density datasets. This has implications for understanding the contribution of the SOR to the atmospheric response to the solar cycle.

Figure c and d show equivalent plots to Fig. a and b for SAGE II v6.2 and v7.0 in coordinates of mixing ratios on pressure levels. The SORs between ∼ 50 and 10 hPa are very similar in the two versions and strongly resemble Fig. a and b, with a positive SOR in the tropical lower stratosphere of ∼ 1–2 %. The structures of the SOR between 20 and ∼ 7 hPa are also similar, with subtropical maxima of 1–2 % and a distinct equatorial minimum. However, the SORs in the upper stratosphere are markedly different between v6.2 and v7.0. Polewards of ±20∘ the structure of the SORs are similar in both datasets, but the magnitude is ∼ 1 % larger in v6.2. In the tropics, the v6.2 mixing ratio data show a large peak in the SOR in the upper stratosphere of up to 5 %, whereas the v7.0 data show a smaller SOR of 1 % in this region.

The confidence intervals for the SAGE II mixing ratio SORs in Fig. c and d are generally similar to those for number densities, with the exception of the uncertainties being considerably larger in the tropical upper stratosphere in both datasets but particularly in SAGE II v6.2. The relatively large uncertainties in the best estimate of the SOR would feed through to similar uncertainties in the contribution of the SOR to the atmospheric response to the 11-year solar cycle . It is therefore important to understand the causes of the differences in SOR between the SAGE II v6.2 and v7.0 mixing ratio datasets, since this presents a limitation for understanding and simulating the climate response to solar forcing (e.g. ). This is explored in the next section.

Given the uncertainties in the SOR diagnosed for SAGE II mixing ratios discussed above, we focus our analysis of the extended SAGE II records on the three SI2N datasets that are currently available as number densities (see Sect. ): SAGE–GOMOS 1, SAGE–GOMOS 2, and SAGE–OSIRIS. Extending SAGE II using these more recent measurements increases the number of data points included in the MLR model by almost a factor of 2 in the tropics and by ∼ 50 % in the subtropics (see Figs. S1 and S2 in the Supplement). Figure  shows time series of monthly tropical percent ozone anomalies at select altitudes for the three SI2N datasets. The datasets do not agree perfectly over the SAGE II era (1984–2004) because the anomalies are defined relative to the entire time series, but overall they show similar behaviour to SAGE II v7.0 number densities (green line) in Fig. , as expected. In the post-2004 period, where either GOMOS or OSIRIS data are included, the datasets show generally consistent behaviour in the mid-stratosphere during the overlap period up to 2011. QBO-like variations in ozone are visible in the time series at 24 and 31 km. At 36 km, there is a decline in ozone from 2004 to 2009 in all three datasets, with increases subsequent to this. However, in the upper stratosphere (48 km), there are more substantial differences between the datasets, particularly between the SAGE–GOMOS 1/2 and SAGE–OSIRIS records. SAGE–OSIRIS shows mean positive anomalies from 2004 to 2013, particularly in the latter part of the record, whereas the two SAGE–GOMOS datasets show negative anomalies between 2007–2010, which coincide with the minimum of solar cycle 23. These differences in ozone variability during the post-SAGE II period may affect the SORs diagnosed in the extended datasets, as compared to the SAGE II v7.0 data alone (Fig. b).

Figure a–c show the SORs in the three extended SAGE II datasets, and Fig. d–f show their associated 95 % confidence intervals in terms of percent ozone. An indication of the importance of how the satellite records are merged for the SOR can be seen by comparing Fig. a and b, which show SAGE–GOMOS 1 and SAGE–GOMOS 2, respectively. The SOR in SAGE–GOMOS 1 shows a generally smoother spatial structure as compared to SAGE–GOMOS 2, although the magnitudes are not distinguishable from one another given the estimated confidence intervals (Fig. d–e). Nevertheless, since statistical uncertainties in the SOR are not typically accounted for in solar-climate studies (e.g. ) or in climate model ozone datasets (e.g. ), differences in the best estimate of the SOR between the datasets remain important to characterize. The differences in SOR between SAGE–GOMOS 1 and 2 must arise from differences in the data merging procedures, which are summarized by and are described in detail by and . Analysis of the SOR in the two SAGE–GOMOS datasets over the SAGE II period alone (1984–2004) reveals similar differences in magnitude and structure (not shown), which suggests that the use of SAGE II or GOMOS as a reference to which the other record is adjusted is a key factor for the differences in SOR in Fig. a and b. The uncertainties in the SOR in SAGE–GOMOS 2 (Fig. e) are similar to those found in the SAGE II v7.0 number density dataset (Fig. b), whereas the magnitude of the uncertainties in the SOR in SAGE–GOMOS 1 (Fig. d) are reduced compared to SAGE II v7.0, particularly in the upper stratosphere.

The SOR in the SAGE–OSIRIS dataset (Fig. c) shows significant positive values in the subtropics between ∼ 30 and 40 km. This is consistent with the results of who conducted a similar MLR analysis to assess long-term ozone trends in SAGE–OSIRIS (see also ). However, the SOR is smaller and less significant in the tropical upper stratosphere and northern extratropics as compared to the two SAGE–GOMOS datasets and the SAGE II v7.0 data. identified a significant positive drift of 5–8 % decade-1 in OSIRIS data above 35 km compared to ozonesondes and lidar measurements, which may contribute to the differences in SOR in the upper stratosphere.

Although there are broad similarities in the SOR between the three extended SAGE II datasets there are also important differences. This is despite the fact that all of the datasets use SAGE II v7.0 number densities as a basis. There is therefore a trade-off between generating the longest climate data record possible, which is desirable for analysing quasi-decadal signals, and the introduction of additional sources of uncertainty from combining multiple satellite records with different sampling properties and drifts . There appear to be variations in ozone in the OSIRIS record that reduce the magnitude of the SOR in the extended SAGE–OSIRIS record compared to the SAGE II period alone. When the SAGE–GOMOS datasets are analysed over the SAGE II period (1984–2004; not shown), SAGE–GOMOS 1 shows the greatest resemblance to the original SAGE II v7.0 data in Fig. b and is characterized by the smallest uncertainties of the three datasets in the upper stratosphere, and we therefore conclude that this record is likely the most reliable estimate of the SOR from the extended SAGE II datasets considered.

Figure  shows time series of tropical monthly percent ozone anomalies at select stratospheric levels (as in Fig. ) for the SBUVMOD VN8.0 (black line), SBUVMOD VN8.6 (red line), and SBUV Merged Cohesive VN8.6 (blue line) datasets. At 1 hPa, the ozone anomalies in the different SBUV datasets are in good agreement between 1979 and 1994. After 1994, the main differences are found between the SBUVMOD VN8.0 and the two SBUV VN8.6 datasets, the latter being largely consistent with one another. In particular, SBUVMOD VN8.0 shows a larger positive trend in ozone from the mid-1990s to the mid-2000s than in the SBUV VN8.6 records; this partly coincides with the ascending phase of solar cycle 23. At 3 hPa, a comparison of the three SBUV records reveals somewhat different behaviour. Here, the SBUVMOD VN8.0 and SBUV Merged Cohesive VN8.6 datasets show similar ozone variations, and instead the SBUVMOD VN8.6 is an outlier exhibiting a larger decline in ozone compared to the other two records of ∼ 7–8 % over the period 1979–2012. At 5 hPa, the three SBUV datasets generally show similar temporal variations in ozone in the early and latter parts of the records, with some differences in offsets linked to different behaviours in the late 1990s and early 2000s when data come from the NOAA-11, 14, 16, and 17 satellites. At 30 hPa, the three SBUV records are largely consistent with one another in their short- and long-term variations, with some exceptions during the 1990s when the data come mainly from the NOAA-11 and NOAA-14 satellites (see, e.g., ).

Figure a, b, and c show the annual mean SORs in the SBUVMOD VN8.0, SBUVMOD VN8.6, and SBUV Merged Cohesive VN8.6 datasets, respectively. Figure d–f show the associated 95 % confidence intervals in terms of percent ozone. All three SBUV records show a significant positive SOR in some parts of the upper stratosphere of up to 2–3 %. The SOR in the tropical upper stratosphere is smaller and not highly statistically significant in SBUVMOD VN8.6, which is in contrast to the two other records and somewhat resembles the SOR in SAGE II v7.0 mixing ratios (Fig. e). The modifications to the data processing algorithm between SBUVMOD VN8.0 and SBUVMOD VN8.6 are documented by ; these include the use of new ozone absorption cross sections, a new a priori ozone climatology, and a new cloud-height climatology. In addition, changes were also made to the inter-instrument calibration, which is now achieved at the radiance level during periods of overlap between the SBUV instruments . Note that the difference in SOR in the tropical upper stratosphere between the two SBUV VN8.6 records remains when SBUVMOD VN8.6 is analysed over the shorter 1978–2012 period (not shown), so this does not result from the inclusion of the early BUV measurements in SBUVMOD VN8.6.

The SORs in the three SBUV records show further differences between 10–50 hPa, with SBUVMOD VN8.6 showing a larger and more significant SOR, particularly in the northern extratropics, while SBUV Merged Cohesive VN8.6 shows a weaker SOR. However, we note that the poor vertical resolution (∼ 10 km) of the SBUV instruments at pressures greater than ∼ 15 hPa makes it challenging to resolve features in the mid and lower stratosphere. Note that the confidence intervals for all the SBUV records are smaller than those for SAGE II-based records (see Figs.  and d–f). This is likely to be because the number of data points included in the MLR analysis is around 2–3 times higher for the SBUV datasets than for the SAGE II records (see Figs. S1 and S3 in the Supplement).

It is desirable for the purposes of, e.g., chemistry–climate model evaluation to determine which SBUV dataset might be most reliable for estimating the annual mean SOR. analysed total column ozone measurements from SBUVMOD VN8.0 and SBUVMOD VN8.6 and found a smaller SOR in SBUVMOD VN8.6 near-global column ozone, which appeared to be related to instrument effects around the 1996 time frame. However, analysed the SOR in total column ozone data and found that most of the signal is associated with ozone changes in the lower stratosphere that are linked to dynamical processes. Column ozone measurements are therefore unlikely to be particularly helpful for constraining the SOR in the upper stratosphere, where differences are found amongst many of the datasets analysed here and where the SOR is strongly determined by photochemical processes.

analysed vertical profiles of long-term ozone trends in satellite datasets and found that SBUV Merged Cohesive VN8.6 showed negligible ozone trends at 2 hPa over 1984–1997, whereas almost all other datasets analysed, including SBUVMOD VN8.6, showed a significant decline of several percent per decade over this period. Instead, SBUV Merged Cohesive VN8.6 showed larger negative ozone trends that the other datasets between 5–10 hPa. and explain how the adjustments used to combine data from the ascending node of NOAA-11 with NOAA-9 and NOAA-14 in SBUV Merged Cohesive VN8.6 were determined from the overlap of the descending node of NOAA-11 with NOAA-16 because of known issues with the quality of data from NOAA-9 and NOAA-14 . Since the NOAA-9 and NOAA-14 data coincide with the end of the trend analysis period used by , this could have had a particularly pronounced impact on their linear trend calculations but may not be as important for diagnosing the SOR.

From the time series of 1 hPa ozone anomalies shown in Fig. , it would appear that differences between the two SBUV VN8.6 datasets in the early 2000s may be more important for determining the differences in SOR in the tropical upper stratosphere. During this period, which coincides with the maximum of solar cycle 22, SBUVMOD VN8.6 shows persistently more negative ozone anomalies than SBUV Merged Cohesive VN8.6. Further analysis of the SOR for the period up to the year 2000 (not shown) does produce a slightly larger and more significant SOR in the tropical upper stratosphere in SBUVMOD VN8.6, but the magnitude is still ∼ 1 % smaller than in SBUV Merged Cohesive VN8.6, indicating that the post-2000 period alone does not explain all differences between Fig. b and c. Based on the above factors, it is difficult to assert which of the SBUV VN8.6 datasets is likely to be most reliable for estimating the SOR. However, in practice the differences between the SORs in the tropical upper stratosphere in the SBUV records are small compared to their associated statistical uncertainties (Fig. d–f) and small compared to the differences in SOR between the two SAGE II mixing ratio datasets in this region. We therefore conclude that using the longest climate data record is most favourable for diagnosing the SOR, particularly on seasonal timescales (see Sect. ), and we therefore adopt SBUVMOD VN8.6 for this purpose.

The analysis thus far has described the annual mean SOR in satellite ozone datasets. However, the SOR is expected to exhibit a seasonal dependence; for example, in regions close to photochemical steady state the annual cycle in solar zenith angle would be expected to produce a larger SOR in the summer hemisphere . Furthermore, given the hypothesis that solar variability modifies the strength of the stratospheric polar vortex , there may also be seasonal signatures in the SOR arising from changes in ozone transport, particularly in the winter hemispheres. Seasonal variations in the SOR could potentially influence the climate response to solar forcing through coupling to radiation (e.g. ), and it is therefore important to characterize these in observations and chemistry–climate models.

Constraining the SOR on seasonal timescales requires high spatial and temporal data coverage; this is to ensure that any seasonal component of the signal can be resolved but also to increase the number of degrees of freedom (i.e. the number of data points) available for the regression. Such coverage is not adequately provided by limb-viewing instruments, such as SAGE II, which have relatively sparse and infrequent sampling. The coverage is considerably better for nadir-viewing instruments like SBUV; however, as described above, their vertical resolution is much poorer in the middle and lower stratosphere. There is therefore a trade-off between the information about the SOR that can be usefully extracted from different data sources.

Given the denser sampling of SBUV compared to SAGE II, we focus here on the SBUVMOD VN8.6 dataset to examine the seasonality of the SOR. Figure  shows the monthly SOR in SBUVMOD VN8.6 for the period 1970–2012. These values are calculated by applying the MLR model to time series for individual months, and therefore no autocorrelation term has been included, since separate months are approximately uncorrelated from year to year. We note that the detailed magnitudes and structure of the monthly SORs are more sensitive to the choice of analysis epoch than for the annual mean SOR (not shown), but the broad features generally remain consistent. The key point to take from Fig.  is that there are substantially enhanced meridional and vertical gradients in the monthly SORs as compared to the annual mean SOR for SBUVMOD VN8.6 in Fig. b. This is similar to the conclusion reached by .

Although much of the localized variations in the SOR are driven by dynamical processes, it is also possible that they could feedback onto circulation through the radiative impacts of ozone on stratospheric heating rates and temperatures. concluded that the three chemistry–climate models from CMIP5 that simulate strong gradients in ozone in the winter upper stratosphere, which most closely resemble observations, tend to have high-latitude dynamical responses to the solar cycle that are most similar to reanalysis data. Seasonal variations in the SOR may therefore play a role in the ability of a model to simulate the climate response to solar variability. However, given the tight coupling between ozone and dynamics at higher latitudes, the attribution of the importance of such radiative feedbacks is particularly challenging. To our knowledge, the importance of this two-way coupling for the climate response to solar variability has not been explicitly tested. This is important to clarify because it is not known whether it is sufficient to simply prescribe a seasonally varying SOR or whether a fully interactive chemistry–climate model is required to adequately capture the feedbacks between composition, radiation, and dynamics that lead to an amplified regional response to the solar cycle. The representation of the SOR in global climate models is discussed in more detail in Part 2 of this study (Maycock et al., 2016).