Study of the dependence of stratospheric ozone long-term trends on local solar time

Multi-instrument comparison analyses are essential to assess the long-term stability of data records by estimating the drift and bias of instruments. The ozone profile dataset from the SOMORA microwave radiometer (MWR) in Payerne, Switzerland, was compared with profiles from the GROMOS MWR in Bern, Switzerland, satellite instruments (MLS, MIPAS, HALOE, SCHIAMACHY, GOMOS), and profiles simulated by the SOCOL v3.0 chemistry-climate model (CCM). The Payerne MWR dataset has been homogenized to ensure a stable measurement contribution to the ozone profiles and to take 5 into account the effects of three major instrument upgrades. At pressure levels smaller than 0.59 hPa (above ∼50 km), the homogenization corrections to be applied to the Payerne MWR ozone profiles are dependent on local solar time (LST). Due to the lack of reference measurements with a comparable measurement contribution at a high time resolution, a comprehensive homogenization of the sub-daily ozone profiles was possible only for pressure levels larger than 0.59 hPa. The long-term stability and mean biases of the time series were estimated as a function of the measurement time (dayand 10 nighttime). The homogenized Payerne MWR ozone dataset agrees within ±5% with the MLS dataset over the 30 to 65 km altitude range and within ±10% of HARMOZ datasets over the 30 to 65 km altitude range. In the upper stratosphere, there is a large nighttime difference between Payerne MWR and other datasets, which is likely a result of the mesospheric signal aliasing with lower levels in the stratosphere due to a lower vertical resolution at that altitude. Hence, the induced bias at 55 km is considered an instrumental artefact and is not further analyzed and discussed. 15 In the upper stratosphere (5–1 hPa, 35–48 km), the Payerne MWR trends are significantly positive at 2 to 3 %/decade. This is in accordance with the northern hemisphere (NH) trends reported by other ground-based instruments in the SPARC LOTUS project. The reason for variability in the reported long-term ground-based and satellite ozone profile trends has multiple possibilities. To determine what part of the variability comes from measurement timing, MWR trends were estimated for each hour of the day with a multiple linear regression model to quantify trends as a function of LST. In the midand upper 20 stratosphere, differences as a function of LST are reported for both the MWR and simulated trends for the 2000-2016 period. 1 https://doi.org/10.5194/acp-2020-101 Preprint. Discussion started: 17 February 2020 c © Author(s) 2020. CC BY 4.0 License.

chemistry-climate models and the large uncertainties on the trends show that it is difficult to clearly provide evidence of stratospheric ozone recovery (Steinbrecht et al., 2017;Ball et al., 2017;Petropavlovskikh et al., 2019). Besides information on the stability and drift of the measurements (Hubert et al., 2016), consideration of the ozone diurnal variability (Studer et al., 2014) and of the spatial and temporal sampling of the dataset  is of primary importance to understanding differences in the trends and to reduce the uncertainty associated with them. The variation of the trends with 70 LST has to be quantified to calculate representative trends derived from sun-synchronous satellite measurements and ensure a proper comparison of these trends with those estimated from ground-based instruments, or to ensure a proper comparison of trends estimated from instruments with different measurement schedules. The consistent high time resolution of ground-based MWR instruments make them ideal for excluding this sampling bias from the parameters influencing trend estimates.
In this study, we first carefully homogenize the SOMORA Payerne MWR ozone profile dataset and validate this dataset 75 against simultaneous ozone profiles measured by satellites. We then investigate the effect of the ozone diurnal cycle on the post-2000 linear trend by calculating long-term trends for each hour of the day and night with a multiple linear regression (MLR) model. Finally we compare the SOMORA Payerne MWR ozone trends in the upper and middle stratosphere to trends of OH, NO x and T derived from the SOCOL v3.0 CCM, to investigate the correlations between the long-term variability of ozone, water vapor, nitrous oxide, and temperature, particularly at the diurnal scale. 80 The paper is organized as follows: Section 2 describes the ozone datasets measured by individual instruments. Section 3 is dedicated to the description of the methods used to calculate the diurnal cycle and trends. In Sect. 4, we describe and discuss the results of the SOMORA Payerne MWR validation and of the dependence of the MLR trends as a function of the time of day. Conclusions about the variability of the long-term stratospheric ozone trends are provided in Sect. 5. The microwave radiometer SOMORA, hereafter referred to as the Payerne MWR, is located in Payerne (46.82 • N, 6.95 • E, 491 m), Switzerland, and has been operated continuously by MeteoSwiss since January 2000. Ozone profile retrievals are obtained independent of weather conditions throughout the diurnal cycle, forming a dataset with a constant and stable time sampling over two decades. Ozone profiles are provided in volume mixing ratio (ppmv) on a pressure grid between 47.3 and 0.05 hPa. 90 The vertical resolution is 8-10 km from 47 to 1.8 hPa (20 to 40 km), increasing to 15-20 km at 0.18 hPa (60 km) (Maillard Barras et al., 2015). The measurement contribution is above 80% from 47 to 0.27 hPa (20 to 57 km). The Payerne MWR is included in the Network for the Detection of Atmospheric Composition Change (NDACC).

Measurement principles and profile retrieval
Developed in 2000 by the University of Bern (Calisesi, 2000), the Payerne MWR is a total power microwave radiometer 95 measuring the thermal emission line of ozone at 142.175 GHz. The electromagnetic radiation is measured at an antenna elevation angle of 39 • and the brightness temperatures range from 80 to 260 K. The Payerne MWR is calibrated using a hot load heated and stabilized at 300 K and a cold load at 77 K cooled with liquid nitrogen. A rotating planar mirror is used as a switch between the radiation sources. A Martin-Puplett interferometer (sideband filter) picks out the frequency band around 142 GHz. Outgoing from the front-end part (quasi optics), the signal is amplified and down-converted in frequency 100 to 7.1 GHz (mixer) by means of a constant-frequency signal (GUNN oscillator). The signal is further down-converted in two steps (intermediate step at 1.5GHz/1GHz) to the baseband (0-1 GHz). The spectral distribution, i.e. voltage as a function of channel or frequency, is measured by Acousto-optical spectrometers (AOS) in the first decade and since then by an Acquiris Fast-Fourier-Transform spectrometer (FFTS) with 16384 channels distributed over 1GHz bandwidth.
The pressure broadening effect on the line allows the retrieval of the vertical ozone profile from the measured spectrum using 105 an a priori profile, a radiative transfer simulation (forward model, ARTS ), and the optimal estimation method (OEM, Qpack ) based on Rodgers (2000).
The required a priori information is taken from a monthly-varying climatology (called the ML climatology and described in McPeters and Labow (2012) formed by combining data from Aura MLS (2004 with data from balloon radiosondes ). Ozone below 8 km is based on sonde measurements, from 16 km to 65 km it is based on MLS measurements, 110 and above 65 km a climatological standard profile combining 5 satellite ozone datasets is used (described in Keating et al. (1990)). Radiosonde and MLS data are blended in the tropospheric transition region. The ML climatology is combined with the Keating standard profile in the mesospheric transition region.
The diagonal elements of the a priori covariance matrix are given by the variance of the ML climatology. The off-diagonal elements are parameterized with an exponentially decaying correlation function using a correlation length of 3 km. The di-115 agonal elements of the error covariance matrix of the measured spectrum are estimated from the variance of the wings of the measured spectrum. The off-diagonal elements of the measurement error covariance matrix are zero.
The retrieval is characterized by the averaging kernel (AVK) matrix describing the changes in the retrieved profile as a function of changes in the true profile. The width of the AVKs is a measure of the vertical resolution of the retrievals and the area of the AVKs indicates the measurement contribution to the retrieved profile (Rodgers, 2000). The ozone profile is 120 considered as reliable when the measurement contribution (MC) dominates the a priori information, i.e. when the measurement contribution is higher than 80%. Figure 1 shows the Payerne MWR AVKs and the MC (×0.5) for one sample month (January 2013).

Data quality and reliability
The total uncertainty is calculated for each retrieved profile accounting for the following sources of uncertainty: measurement 125 noise, tropospheric attenuation, calibration load temperatures, spectroscopy, atmospheric temperature profile, and smoothing.
The dominant source of uncertainty is smoothing because of the instrument's limited vertical resolution and is on the order of 15-20% of the ozone content. The second most important contribution is the measurement noise on the spectra amounting to 3-7% error when using a standard integration time of 1 hour. Tropospheric attenuation correction, the pressure-broadening coefficient of the observed line, calibration loads temperatures, and the atmospheric temperature profile (Payerne radiosondes 130 combined with the ECMWF ERA-interim data) amount to an uncertainty of less than 3%.

Homogenization of the Payerne MWR AOS and FFTS time series
Spectral analysis of the Payerne MWR measurements was performed using two acousto-optical spectrometers (AOS) from January 2000 to October 2010; the AOS had a total bandwidth of 1 GHz, with a frequency resolution varying from 24 kHz at 135 the line center to 980 kHz at the wings. In September 2009, an Acquiris fast Fourier transform spectrometer (FFTS) was added as a back end to the Payerne MWR. The FFTS covers a total bandwidth of 1GHz with 16384 channels, providing a frequency resolution of 61 kHz. This technical upgrade introduced a discontinuity in the timeseries, requiring an homogenization of the dataset. The AOS and FFTS were used in parallel for one year to ensure a proper homogenization of the transition.
The measurement contributions to simultaneous ozone profiles retrieved from the AOS and FFTS measurements were com-140 pared for the overlap period of one year. In Fig. 2 the monthly means of the measurement contribution at 0.4 hPa as a % of the ozone profile are plotted against the monthly means of the diurnal cycle amplitude (maximum-to-minimum difference) as a % of the midnight ozone value for the 2000-2016 measurement period. A clear negative correlation between the amplitude of the ozone diurnal cycle and the measurement contribution is shown for the lower mesosphere. A low measurement contribution means there is a large a priori contribution. Since the a priori profile is a standard climatological ozone profile without any 145 diurnal variation, the lower the measurement contribution, the lower the diurnal cycle amplitude. Since the ozone diurnal cycle amplitude is correlated to the measurement contribution value, a proper homogenization should not be limited to a correction 5 https://doi.org/10.5194/acp-2020-101 Preprint. Discussion started: 17 February 2020 c Author(s) 2020. CC BY 4.0 License. of the bias between profiles retrieved from the spectra measured by the 2 spectral setups but should include a full homogenization of the characteristics of the retrieval (measurement response and vertical resolution). The AOS and FFTS ozone profile datasets used in this study were harmonized by ensuring a constant measurement contribution to the retrieved ozone profiles 150 between 47 and 0.05 hPa (20 and 70km). The integration time for one ozone profile therefore varies from 30 min to 2 h.
For a stable measurement contribution over the AOS to FFTS transition in the upper stratosphere/lower mesosphere, FFTS measurements require being accumulated over 2 h (FFTS2h dataset). However, in the altitude range where the measurement contribution is much larger than 80%, a FFTS signal accumulation time of 1 h is sufficient, therefore allowing a higher time resolution dataset (FFTS1h dataset). The FFTS1h data are used for the middle stratosphere and upper stratosphere below 48km 155 where high time resolution without any degradation of the measurement contribution is required. The FFTS2h data are used for the lower mesosphere where the consistency of the measurement contribution is more important than the time resolution requirement.
The bias between the profiles retrieved from the two spectral measurement setups was then determined from simultaneous measurements during the one year transition period. The mean absolute difference for each profile layer was subtracted from 160 the AOS profiles, keeping the FFTS profile dataset unchanged. Bias values are within 10% between 47 and 0.05 hPa (20 to 70km).
The application range of the correction offset depends to a large extent on the way the offset is calculated. The AOS to FFTS correction offset, when determined by the comparison of simultaneous monthly means, should be applied only to the global monthly means time series and not to sub-daily monthly means. To homogenise the AOS to FFTS datasets for both the 1h 165 and 2h bins, the AOS to FFTS correction offset variation with LST was determined. As shown in Fig. 3a and b, the correction offsets do not vary significantly with LST below 50 km (0.6 hPa). However, in the lower mesosphere, the AOS to FFTS 2h correction offset is lower during daytime than during nighttime, following the diurnal variation of ozone. A correction offset depending on the LST has to be applied to the AOS data above 50 km for a proper homogenization of time series when LST depending monthly means are considered. vertical resolution is between 8-12 km in the stratosphere and increases with altitude to 20-25 km in the lower mesosphere.
In order to show the effect of homogenization, please show the deseasonalized timeseries before and after.

Mauna Loa MWR
The Mauna Loa MWR (MLO MWR) operated by U.S. the Naval Research Laboratory measures the emission spectrum of 215 the ozone line at 110.836 GHz. It has been in operation at Mauna Loa (19.54 • N,155.6 • W, 3397 m), USA, since 1995 and also contributes to NDACC. The spectral intensities are calibrated with black body sources at ambient and liquid nitrogen temperatures. The experimental technique is described in Parrish et al. (1992), and technical details about the instrument are provided in Parrish (1994). While the basic radiometric features are similar to the Bern and Payerne MWRs, the MLO MWR receiver is cryogenically cooled and the spectral distribution is measured by a filter bank spectrometer. The ozone mixing ratio 220 profiles are retrieved from the spectra using an adaptation of the optimal estimation method of Rodgers (Connor et al., 1995;Rodgers, 2000).
The vertical resolution is 6 km at an altitude of 32 km, between 6 and 8 km from 20 to 42 km, and then increases to 14 km at 65 km. The measurement contribution is above 80% from 20 to 70 km. While hourly measurements are performed for the purpose of diurnal cycle studies (Parrish et al., 2014), data are deposited in the NDACC with a 6-hourly time resolution. The 225 NDACC dataset is used in this study. The MLO MWR underwent a major spectrometer upgrade between 2015 and 2017. For this study, only data until May 2015 have been used.

Satellites
The Payerne MWR is compared to the three instruments from the ENVISAT satellite: GOMOS (Global Ozone Monitoring by The HARMOZ dataset (Sofieva et al., 2013) is composed of ENVISAT ozone profiles in number density on a common pressure grid, which corresponds to a vertical sampling of 2-3 km. HARMOZ and HALOE data centered 10 • by 10 • around 235 Payerne were selected (equivalent to an area of approximately 1110 km by 760 km). The collocation criterion for MLS satellite data is ±3 • in latitude and ±5 • in longitude (an area of approximately 666 km by 760km). This criterion ensures a sufficient number of collocated measurements and thus provides reliable bias estimates.

MLS
MLS is a microwave limb-sounding radiometer onboard the Aura Earth observing satellite. Ozone profiles are retrieved from 240 MLS radiance measurements at 240 GHz. Details about the Aura mission can be found in Waters et al. (2006). In this study we use ozone profiles from the version 4. GOMOS was a stellar occultation instrument on board ENVISAT from 2002 to 2012 (Bertaux et al., 2010). GBL (Gomos Bright Light) measured the atmospheric limb radiance of scattered sunlight (Tukiainen et al., 2015). Ozone profiles are retrieved using the ESA IPF v6 processor (Sofieva et al., 2017) from the ultraviolet and visible spectrometer measurements at 255 wavelengths between 250 and 692 nm with a two-step inversion (Kyrölä et al., 2010). ENVISAT overpass times for Payerne are 10:00 UTC (GBL dataset) and 22:00 UTC (GOMOS dataset). GOMOS measured ozone profiles from 20-100 km, with a vertical resolution of 2 km below altitudes of 30 km and 3 km above 40 km.
SCIAMACHY was a spaceborne spectrometer that measured the upwelling radiation from the Earth's atmosphere in the UV, visible, near-infrared and shortwave-infrared spectral ranges. A detailed description of the instrument and its measure-260 ment modes can be found in Bovensmann et al. (1999). Ozone profiles used in this study are retrieved using the UBR limb retrieval algorithm (V3_5). SCIAMACHY measured ozone profiles from 15-40 km, with a vertical resolution of 3 km. While measuring, SCIAMACHY passed over Payerne at 10:00 UTC. The MIPAS, GOMOS, GBL and SCIAMACHY profiles are AVK-convolved to the vertical resolution of the Payerne MWR and converted from number density (HARMOZ dataset) to ppm is carried out using ECMWF temperature profiles.  For comparison with coincident Payerne MWR ozone profiles, the HALOE profiles are AVK-convolved to the Payerne MWR vertical resolution.

SOCOL CCM
The SOlar Climate Ozone Links (SOCOL) CCM consists of the middle atmosphere version of the MA-ECHAM general circulation model (Roeckner et al., 2003;Giorgetta et al., 2006), with 39 vertical levels between the surface and 0.01 hPa (∼80 275 km) coupled to the Model for Evaluation of oZONe trends (MEZON) chemistry module (Egorova et al., 2003).Dynamical and physical processes in the CCM SOCOL are calculated every 15 minutes within the model, while full radiative and chemical calculations are performed every two hours. Chemical constituents are transported using a flux-form semi-Lagrangian scheme (Lin and Rood, 1996). Original version includes 41 chemical species interacting participating in 140 gas-phase, 46 photolysis, and 16 heterogeneous reactions. The CCM SOCOL exploits T42 horizontal spectral truncation, which corresponds approxi-280 mately to 2.5 • by 2.5 • resolution. The first model version was described and evaluated by Stenke et al. (2013). The model now includes an isoprene oxidation mechanism (Poeschl et al., 2000), the online calculation of lightning NOx emissions (Price and Rind, 1992), treatment of the effects produced by different energetic particles (Rozanov et al., 2012), updated reaction rates and absorption cross sections (Sander, 2011), improved solar heating rates (Sukhodolov et al., 2014), as well as a parameterization of cloud effects on photolysis rates (Chang et al., 1987). All considered halogenated ozone depleting species are transported as 285 separate tracers.
The SOCOL v3.0 dataset has been validated in the troposphere and stratosphere with satellites, NDACC ground-based instruments and other CCMs (Staehelin et al., 2017;Revell et al., 2015;Stenke et al., 2013). Stratospheric ozone trends derived from SOCOL v3.0 in specified dynamics mode have also been compared with trends derived from measurements (Ball et al., 2018). For the comparison with ground-based observations the model outputs were horizontally interpolated to the location of 290 Payerne from the adjacent grid cells. An AVK convolution was also applied to the model data for comparison with the Payerne MWR profiles.

Diurnal cycle calculation
The amplitude of the ozone diurnal cycle depends on altitude and varies seasonally. Above 0.59 hPa (50 km), the daytime 295 ozone values are 15-25% lower compared to the nighttime values, while at 5 hPa (35 km) afternoon values are up to 3% larger compared to nighttime values. The ozone diurnal cycle in the mesosphere has been intensively studied (Pallister and Tuck, 1983;Ricaud et al., 1996;Vaughan, 1984). The diurnal cycle of ozone in the stratosphere has been reported in Haefele et al. (2008), and the interannual variations of the ozone diurnal cycle described in Studer et al. (2014).
The diurnal ozone cycle is calculated relative to the nighttime value by: where O 3 ,midnight is an average of the ozone values from 22:00 to 02:00 LST at each pressure level.

Trend calculation
The results are given as a %

Validation
Since the ozone profile time series used in this study is retrieved with an updated version of the OEM Arts/Qpack retrieval (see subsection 2.1), we carried out a new validation of the Payerne MWR dataset with ground-based and satellites ozone profile measurements. The mean relative difference of daytime profiles compared to MLS is approximately between -8 and +8% over the 30-65 km altitude range, while the relative difference compared to the HARMOZ datasets is between -10% and +10% over the 30-65 km altitude range. A systematic positive bias of the MIPAS, GBL, GOMOS, SCIAMACHY, HALOE and Bern MWR day and night datasets compared with the Payerne MWR is reported below 30 km. The larger relative difference compared to the HARMOZ datasets (when compared to the 5% difference with MLS) can be explained by the choice of the spatial coincidence criteria.

340
The criteria for spatial coincidence (10 • by 10 • ) may seem large but is necessary given the smaller number of coincidences.
Reducing the spatial coincidence criterion from 10 • to 6 • in latitude reduces the number of matches by more than 50%.
However, the spatial distribution of matches is uneven, with a large number on the north and south borders of the box. This induces a larger bias, given the ozone gradient at these latitudes. The high density of the MLS measurements means a reduced area can be used for the spatial coincidence criteria without degrading the statistics.

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Below 48km, the nighttime differences compared to MLS, GOMOS, and MIPAS are within ±5%. Above 50km, the relative differences are as large as -7% compared to MLS and -15% compared to MIPAS and GOMOS. The Payerne MWR overestimates nighttime ozone at 55 km compared to all the satellite profiles except HALOE. The positive bias at night at 55km was reported by Hocke et al. (2007) for a previous version of the Payerne retrieval. Rüfenacht and Kämpfer (2017) show that the emission signal of the secondary ozone layer can alter the nighttime ozone retrieval and thus the ozone values above 0.2 350 hPa can be overestimated. The a priori profiles and standard deviations used in the Payerne retrieval are identical for day-and nighttime conditions leading to the effects on the nighttime ozone profiles described in Rüfenacht and Kämpfer (2017), i.e. an overestimation of ozone at 55 km. Considering that the ozone secondary peak intensity is larger at night and that the width of the AVKs of the Payerne MWR dataset is on the order of 17 km in the lower mesosphere, the ozone information in the profile at 55 km is influenced by the ozone content at higher altitudes.

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The ±10% agreement of the Payerne MWR with satellites assess the quality of the measurements considering the uncertainties of the respective instruments. The systematic 7% underestimation of ozone at 30 km is under investigation, while the nighttime 15% overestimation has been assigned to a mesospheric aliasing effect. The agreement between the two swiss MWRs is described in the next paragraph.

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The mean relative difference between the Bern and Payerne MWRs lies between ±10% up to 40km, increasing up to a maximum of -10 to -13% between 47-57 km. Since the measurement setups of the Bern and Payerne MWRs are very similar we would expect better agreement between the two instruments. However, the Payerne and Bern retrieval procedures differ in the calibration, in the a priori climatology used, as well as in the uncertainties used for the a priori and measurement covariance matrices, the spectroscopic parameters (pressure broadening), and in the temperature profiles used in the forward model. The 365 measurement contribution and total error therefore slightly differ between the two MWRs. Based on a sensitivity study of the retrieval parameters of the Payerne MWR ozone profiles, the influence of the spectroscopic parameters is as high as ±7% at 50 km, while the influence of the forward model temperature profile is between ±2% at 50 km. Moreover, no period weighting to account for anomalous measurements such as described in Bernet et al. (2019) has been applied to the Bern MWR dataset.
determine the sources for the differences.
4.1.1.2 Relative difference compared to SOCOL v3.0 The Payerne MWR and SOCOL climatological means agree within 15% at 50km (Fig. 4). Above this level, the daytime values show differences of similar magnitude, but the nighttime differences are much larger, up to 40% at 0.2 hPa (60km). The relative differences also appears to vary with season, with ozone values being closer in NH winter than in summer. The high 375 time resolution of both the Payerne MWR and SOCOL simulations means that it is possible to derive ozone diurnal cycle profiles (Fig. 5). In the NH mid-latitudes, the daytime ozone levels vary with altitude from +4% at 4 hPa (36 km) to -25% at 0.2 hPa (60 km) with respect to nighttime values. The comparison of ozone diurnal cycles measured by the Payerne MWR and simulated by SOCOL v3.0 show good agreement, both with negative values during the day above 0.85 hPa, a minimum at sunrise, and a maximum in the afternoon, peaking at 12 hPa and 4 hPa respectively for Payerne MWR ( Fig. 5a and b). The   Day-and nighttime mid-stratospheric trends do not differ significantly at the 95% confidence level for the Payerne MWR, 405 SOCOL, MLS satellite, or for the MLO MWR (lower panel of Fig. 7). The largest, but still not statistically significant, difference between day-and nighttime trends is measured at 45 km, with positive trend estimates ranging from 2 to 4%/dec for the Payerne MWR, and from 2 to 5%/dec for MLS. The statistically significant difference measured for the Bern MWR at 50 km is probably artificially produced by the 2009 homogenization which does not take into account the measurement contribution variation and/or a variation of the correction offset with LST. A similar behavior was observed for the Payerne MWR before 410 the comprehensive homogenization of the AOS and FFTS datasets.
No significant differences between trends derived from day-and nighttime measuring instruments can therefore be attributed to a systematic measurement schedule difference between the instruments. As below 45 km, the diurnal cycle amplitude is minimal at sunrise and maximal in the afternoon, we do not expect any influence of the ozone diurnal cycle on the daytime and nighttime long-term trends at these altitudes. The potential influence of the diurnal cycle (ozone morning minimum and 415 afternoon maximum) at these altitudes will be investigated by considering trends for each hour (see Sect. 4.2.2).
Day-and nighttime lower mesospheric trends do not differ significantly at 95% for the Payerne MWR, SOCOL, MLS satellite, or for the MLO MWR (upper panel of Fig. 7). The largest, but still not statistically significant, difference between day-and nighttime trends is measured at 57 km, with trend estimates ranging from 0 to 3%/dec for the Payerne MWR, and at 62 km, with trend estimates ranging from 0 to -2%/dec for MLS. At these altitudes too, no significant differences between trends 420 derived from day-and nighttime measuring instruments can be attributed to a systematic measurement schedule difference, despite the fact that we expected an influence of the ozone diurnal cycle on the trend estimates in this altitude range. The dayand nighttime trends as measured by the Bern MWR are significantly negative in the lower mesosphere. We did not investigate the large negative nighttime trend estimates in this work but, as mentioned in Sect. 4.1.1.1, intensive efforts are ongoing to homogenise the two swiss MWRs for the post-2010 period.

Payerne MWR ozone trends as a function of LST
In the stratosphere the ozone diurnal cycle presents a minimum at sunrise (∼12.5 hPa) and a maximum (∼4.16 hPa) in the afternoon (shown in Fig. 5). Long-term trends for each hour of the day were calculated using the method described in Sect.
3.2. This is only possible for the Payerne and Bern MWR datasets and for the SOCOL v3.0 simulations, which all have the necessary hourly time resolution.

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In Fig. 8, one trend estimate in %/dec is plotted for each pressure level in the stratosphere and each hour of the day without any interpolation. Trend profiles in %/dec are shown as a function of LST, with hatches indicating values which are not significantly different from zero at the 95% confidence level. The Payerne MWR mid-stratospheric 2000-2018 trends are represented in Fig. 8a and Bern MWR trends in Fig. 8b. For each pressure level, the variations of ozone trends as a function of LST are small and the differences are not significant at the 95% confidence level. The largest, but still not statistically 435 https://doi.org/10.5194/acp-2020-101 Preprint. Discussion started: 17 February 2020 c Author(s) 2020. CC BY 4.0 License. shows significant, difference is shown between 4 and 14 h LST at 40 km (Fig. 8c in red). Even when considering the altitude difference between the morning minimum and the afternoon maximum, the long-term ozone trends at the morning minimum (12.5 hPa, 8h LST, in blue) is similar to the long-term ozone trends at the afternoon maximum (4.16 hPa and 14h LST, in black) at the 95% confidence level.

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Based on the correlation between the stratospheric O3 and the NOx, the active H and the Temperature long-term variations, and on the similarities of the ozone diurnal variation simulated by SOCOL and measured by the Payerne MWR, we investigate the variation of the NOx, OH and T trends with LST as an attempt to derive a correlation between ozone trend variation with LST and ozone-influencing substances trends variations with LST. We derive trends of simulated OH, NOx and T as a function of LST in the stratophere. We apply a similar MLR with proxies for the solar cycle, QBO at 30 hPa and 10 hPa, ENSO MEI 445 and aerosols to monthly mean values for the 2000-2016 period. Figure 9a shows the linear trends as a function of LST. SOCOL shows a positive OH trend of 4%/dec during the day and a non significant trend of -2%/dec at night. No significant variation of daytime OH trend with LST is seen in the stratosphere.
The OH impact on ozone chemistry in this altitude range is very limited when compared to NOx and Cl (Schanz, 2015). Active H influence increases from 0.3 hPa up, but is negligible at pressure levels lower than 2 hPa. During the night, OH disappears 450 rather fast due to the absence of photolysis (OH production) and large OH reactivity (OH loss). The nighttime chemistry is mainly influenced by temperature, thus the nighttime ozone trend simulated by SOCOL should be related to the temperature trends ( Fig. 9c) and not to the OH trends. Figure 9b shows the temperature trends as a function of LST. SOCOL shows a slightly negative trend of -1%/dec for both day-and nighttime between 30 km and 43 km. The negative trends are not significant out of this altitude range. Here again, no 455 significant variation of temperature trend with LST is reported. We can only report a negative correlation between the negative temperature trend and the positive ozone trend independent of LST.
In the stratosphere, NOx variability plays a key role in ozone changes (Hendrick et al., 2012;Nedoluha et al., 2015a) with a maximum influence at 4 hPa. A MLR with proxies for the solar cycle, the QBO at 30 hPa and at 10 hPa was applied to monthly means of SOCOL simulated reactive NOx (NOx =NO+NO2) for the period 2000-2016. Figure 9d shows the linear 460 trends in simulated NOx as a function of LST. SOCOL shows a negative trend of -3%/dec in the morning and a statistically nonsignificant negative trend of -1%/dec in the afternoon. The variation with LST is again not significant at the 95% confidence level. The simulated NOx trends are agree with those reported by Hendrick et al. (2012), who shows negative trends of the NOx column for 24h-average datasets. Nedoluha et al. (2015b) demonstrated using HALOE (1991 and MLS (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) measurements that a significant decrease in ozone near 10 hPa in the tropics could be related to a spatially localized, but 465 long-term increase in NOx. Although they also showed that the response of ozone to NOx chemistry varies strongly with time of day (Nedoluha et al., 2015a), with ozone destruction through the NOx cycle increasing from the morning to the afternoon (Schanz, 2015). Here we report negative trends in the morning and in the afternoon, but their difference is not significant at the 95% confidence level. However, when we consider just the global trends without any distinction by LST, we can see a similar negative correlation between the negative NOx trend and the positive O3 trend.

Conclusions
The 2000-2018 Payerne MWR dataset has been reprocessed and harmonized to ensure a constant measurement contribution to the ozone profiles and to take into account the effects of the three major technical upgrades (2001, 2005, and 2009 Adding two more years to the dataset was shown to increase the trends below 30 km. A MLR was also applied to the high 480 resolution data to assess whether significant trends could be detected in the ozone diurnal cycle. Neither stratospheric nor lower mesospheric ozone trends vary with LST significantly at the 95% confidence level. No significant long-term variation of the amplitude of the diurnal cycle is observed even if we consider the altitude difference of the ozone diurnal cycle minimum and maximum. Without any significant variation of the ozone trend with LST, no correlation is possible with the LST variation of temperature, OH and NO x trends. We can only report a negative correlation between the negative NOx trend, the negative