3.1 Monthly zonal means and annual cycle

Figure 1 compares monthly mean total ozone from GOME-2A and SBUV (v8.6) satellite overpass data for stations shown in Table S1. The GOME-2A data were taken at a spatial resolution of $\mathrm{1}{}^{\circ }\times \mathrm{1}{}^{\circ }$ around each of the ground-based monitoring stations listed in Table S1 and then averaged over the tropics, mid-latitudes, and high latitudes of both hemispheres in 30^∘ latitudinal zones to provide the large-scale monthly zonal means for the GOME-2A data. Accordingly, SBUV satellite overpass data were averaged over each geographical zone to provide the large-scale zonal means for the SBUV observations. Mean differences and standard deviations between GOME-2A and SBUV total ozone were found to be $+\mathrm{0.1}\pm \mathrm{0.7}$ % in the tropics (0–30^∘), about $+\mathrm{0.8}\pm \mathrm{1.6}$ % in the mid-latitudes (30–60^∘), about $+\mathrm{1.3}\pm \mathrm{2.2}$ % over the northern high latitudes (60–80^∘ N), and about $-\mathrm{0.5}\pm \mathrm{2.9}$ % over the southern high latitudes (60–80^∘ S). The differences were estimated as (GOME-2A – SBUV)/SBUV (%) from January 2007 to December 2016. Small differences were also found between GOME-2A and GB measurements (Fig. 2 and Table 1), and here GB station data were averaged over each geographical zone to provide the large-scale zonal means for the GB measurements. Mean differences and standard deviations between GOME-2A and GB total ozone were found to be $-\mathrm{0.7}\pm \mathrm{1.4}$ % in the tropics (0–30^∘), $+\mathrm{0.1}\pm \mathrm{2.1}$ % in the mid-latitudes (30–60^∘), $+\mathrm{2.5}\pm \mathrm{3.2}$ % over the northern high latitudes (60–80^∘ N), and 0.0±4.3 % over the southern high latitudes (60–80^∘ S). Recall that all estimates refer to the period between January 2007 and December 2016.

Figure 2Same as in Fig. 1, but for GOME-2A and GB observations. R is the correlation coefficient between the two lines. Error bars show the standard deviation of each monthly mean. Mean differences ±σ are given as (GOME-2A – GROUND)/GROUND (%).

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In summary, the largest differences between GOME-2A, SBUV (v8.6), and GB measurements are found over the northern high latitudes (60–80^∘ N), and the highest variability (standard deviation of the mean difference) is observed over the latitude belt (60–80^∘ S). In addition, these differences (especially at the high latitudes) can be affected by the fact that the same days have not always been used for the construction of the monthly mean values for the different datasets. In the tropics and mid-latitudes the respective differences are within ±1 % or less, in line with Chiou et al. (2014). Validation results were also presented by Loyola et al. (2011), Koukouli et al. (2012), Coldewey-Egbers et al. (2015), and Koukouli et al. (2015), and updates of which are included in Hassinen et al. (2016). Our results based on data updated to 2017 largely confirm those studies, pointing to the good performance of GOME-2A when extending the period of record.

Table 2Statistics of the correlations shown in Figs. 1 and 2 between total ozone from (a) GOME-2A data and SBUV (v8.6) overpass data and (b) GOME-2A data and ground-based measurements.

^* Error, t value, and p value refer to slope.

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Next, we studied the correlation between total ozone from GOME-2A and SBUV satellite data using linear regression analysis for the period 2007–2016. The statistical significance of the correlation coefficients, R, was calculated using the t-test formula for R with N−2 degrees of freedom, as used in Zerefos et al. (2018). The regression model showed statistically significant correlations between the different datasets as follows: $R=+\mathrm{0.99}$ in the tropics, mid-latitudes, and the northern high latitudes and $R=+\mathrm{0.97}$ in the southern high latitudes. All correlation coefficients are highly statically significant (99.9 % confidence level). In the long term, statistically significant correlation coefficients ($R\ge +\mathrm{0.94}$) are also found between GOME-2A satellite and GB measurements (Fig. 2), despite the different type of instruments used to measure total ozone from the ground. The regression parameters for the correlation coefficients shown in Figs. 1 and 2 are provided in Table 2.

A large part of the strong correlations shown in Figs. 1 and 2 is attributable to the seasonal variability of total ozone which is presented in Fig. 3 for GOME-2A, SBUV, and GB data. More specifically, Fig. 3 shows the seasonal variations of total ozone from station data, averaged from zones per 10^∘ latitude, north and south. At high latitudes our analysis stops at 80^∘. There is a very good agreement between the annual cycles of total ozone from the three datasets denoting the consistency of the satellite retrievals with GB observations. Similar annual cycles are also found with the GTO-ECV ozone data (not shown). Similar consistency is also revealed for the amplitudes of the annual cycles, computed as (maximum value – minimum value)/2 in Dobson units (DU). Figure 4 shows global maps of the amplitude of the annual cycle of total ozone for the period 2007–2016 from GOME-2A (panel a), GTO-ECV (panel b), and the TOMS–OMI–OMPS (panel c) satellite data. All maps are plotted against the sine of the latitude north and south in order to show areas according to their actual size. As can be seen from Fig. 4, the amplitude of the annual cycle is less than 20 DU in the tropics, increasing as we move towards the mid-latitudes and high latitudes to up to 75 DU. Interestingly, there is a region with small amplitude of the annual cycle in the southern mid-latitudes with values of about 10–15 DU, seen in Fig. 4 as a blue curved line crossing the longitudes around 60^∘ S, which points to small seasonal variations of total ozone in these parts. The seasonal increase in Antarctic ozone is delayed by 2–3 months compared to the northern polar region. Only with the breakdown of the polar vortex in late spring, i.e. at a time when the poleward transport over lower latitudes has already ceased, does a strong ozone influx occur in the Antarctic. With this delay the amplitude of the seasonal variation stays much smaller poleward of 55–60^∘ in the south than in the north (Dütsch, 1974). These features are consistent between all examined satellite datasets and are reproduced to a large extent by the Oslo CTM3 model as well, except in the southern mid-latitudes, where the model seems to underestimate the observed annual cycle (Fig. 4, panel d).

Figure 3Comparison of the annual cycle of total ozone from GOME-2A with that from SBUV (v8.6) satellite overpass data and GB observations in the period 2007–2016 based on station data averaged per 10^∘ latitude zones. The annual cycle is distorted above 60^∘ S due to the Antarctic ozone hole.

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In summary, we find a similar pattern and amplitude of the annual cycle between total ozone from GOME-2A and the other examined total ozone datasets. The mean differences in the annual cycles of GOME-2A and SBUV satellite data are small in the tropics (0–30^∘: 0.3±2.4 DU) and increase as we move towards the mid-latitudes (30–60^∘: 2.4±4.4 DU) and higher latitudes (60–80^∘: 1.7±4.8 DU). These numbers are consistent with the ones found between GOME-2A and GB measurements (tropics: 1.1±2.3 DU; mid-latitudes: 1.2±5.1 DU; high latitudes: 5.1±7.1 DU). In all latitude zones the correlation coefficients between the annual cycles of GOME-2A–SBUV and GOME-2A–GB data pairs were found to be greater than 0.9.

Before examining correlations with the large-scale natural fluctuations QBO, ENSO, and NAO, the mean annual cycle has been removed from the ozone datasets as described in the next section.

3.2 Correlation with QBO

We then studied how changes in dynamics affect the ozone columns in the atmosphere. The time series obtained have been deseasonalized by subtracting the long-term monthly mean from each individual monthly mean value. Ozone column variations for different latitude zones in the Northern and Southern Hemispheres have been compared. Figure 5 compares total ozone deseasonalized anomalies (in % of the mean) from GOME-2A and SBUV satellite retrievals in the tropics (10^∘ N–10^∘ S), subtropics (10^∘–30^∘ N and 10^∘–30^∘ S), and mid-latitudes (30^∘–60^∘ N and 30^∘–60^∘ S). The right panel of Fig. 5 shows the respective anomalies from GTO-ECV data. Mean differences between GOME-2A and SBUV deseasonalized monthly zonal means between 60^∘ N and 60^∘ S are less than ±0.5 %.

The dotted line superimposed on the ozone anomalies in Fig. 5 shows the equatorial zonal winds at 30 hPa, which were used as a proxy index to study the impact of QBO on total ozone. The general features include a QBO signal in total ozone at latitudes between 10^∘ N and 10^∘ S, which almost matches with the phase of QBO in the zonal winds. At higher northern and southern latitudes there is a phase shift in the QBO impact on total ozone. The impact of QBO is most pronounced in the tropics and is less pronounced in the subtropics and mid-latitudes. Strong positive correlations with the QBO are found in the tropics (correlation between GOME-2A and the QBO is about +0.77, t test =12.91) and weaker (usually of the opposite sign), less significant correlations are found at higher latitudes (about −0.15 in the northern extratropics and about −0.45 in the southern extratropics). Similar correlation patterns with the QBO are found for the GTO-ECV, SBUV, and GB data. These correlations suggest that the variability that can be attributed to the QBO in the tropics is about 60 % and is about 2 % and 20 % in the northern and the southern extratropics, respectively.

Figure 4Comparison of the amplitude, i.e. (maximum value – minimum value)/2, of the annual cycle of total ozone from GOME-2A (a) with the amplitude of the annual cycle of total ozone from GTO-ECV (b), the combined TOMS–OMI–OMPS satellite data (c), and Oslo CTM3 model simulations (d).

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Figure 5(a) Time series of deseasonalized total ozone from GOME-2A and SBUV (v8.6) satellite overpasses over different latitude zones, along with the equatorial zonal winds at 30 hPa as an index of the QBO; (b) same as in (a), but for GTO-ECV and SBUV. Values with red colour refer to the mean differences ±σ (in %) between GOME-2A and SBUV deseasonalized data averaged over various WOUDC stations (150 stations in the northern mid-latitudes – 30–60^∘ N; 21 stations in the northern subtropics – 10–30^∘ N; eight stations in the tropics – 10^∘ S–10^∘ N; 10 stations in southern subtropics – 10–30^∘ S; and 12 stations in the southern mid-latitudes – 30–60^∘ S). The QBO proxy is superimposed on the ozone anomalies.

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Table 3Statistics of correlations between deseasonalized total ozone and the QBO at 30 hPa for (a) GOME-2A data, (b) GTO-ECV data, (c) SBUV (v8.6) overpass data, and (d) ground-based measurements.

^* The slope is in  % per unit change of the explanatory variable. Error, t value, and p value refer to slope.

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Table 3 summarizes the correlation and regression coefficients between total ozone and the QBO at 30 hPa for the different latitude zones and the different datasets. For latitudes between 10^∘ N and 10^∘ S correlations between total ozone from GOME-2A, GTO-ECV, SBUV, GB data, and the QBO are all positive. At latitudes between 10 and 30^∘ the correlations turn to negative, in agreement with the results of Knibbe et al. (2014), who noted that when moving from the tropics towards higher latitudes, the regression estimates switch to negative values at approximately 10^∘ N and 10^∘ S. The correlations with the QBO at 30 hPa remain negative up to 60^∘, a consistent result among all our datasets and something also reported by Knibbe et al. (2014) with the MSR ozone data. The correlation and regression coefficients between GOME-2A and the QBO are fairly similar to those found between SBUV and the QBO, as well as among all datasets as seen in Table 3, despite the different periods of records.

Figure 6Same as in Fig. 5, but for GOME-2A and GB observations (a) and for GTO-ECV and GB observations (b). The QBO proxy is superimposed on the ozone anomalies.

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These features are also evident in Fig. 6, which compares GOME-2A (and GTO-ECV) satellite total ozone with GB observations with respect to the QBO. Mean differences and standard deviations between GOME-2A and GB and between GTO-ECV and GB deseasonalized total ozone data do not exceed 1 %. Again, correlation coefficients between deseasonalized GOME-2A and deseasonalized GB data are highly significant in all latitude zones (30–60^∘ N, +0.91: slope =0.818, error =0.035, t value =23.466, and N=119; 10–30^∘ N, +0.91: slope =0.786, error =0.033, t value =23.529, and N=119; 10^∘ N–10^∘ S, +0.94: slope =0.973, error =0.034, t value =28.449, and N=109; 10–30^∘ S, +0.87: slope =0.864, error =0.044, t value =19.659, and N=119; 30–60^∘ S, +0.88: slope =0.858, error =0.043, t value =19.854, and N=119). The same is true for the correlations between GTO-ECV and GB data pairs (30–60^∘ N, +0.94; 10–30^∘ N, +0.89; 10^∘ N–10^∘ S, +0.94; 10–30^∘ S, +0.87; 30–60^∘ S, +0.85). Our results are in line with Eleftheratos et al. (2013) and Isaksen et al. (2014), who compared QBO-related ozone column variations from the chemical transport model Oslo CTM2 with SBUV satellite data for shorter time periods. In summary, it has been shown that GOME-2A depicts the significant effects of QBO on stratospheric ozone in concurrence with SBUV and GB measurements. The instrument captures the variability of ozone in the tropics and the mid-latitudes correctly, which is nearly in phase with the QBO in the tropics and out of phase in the northern and the southern mid-latitudes, as has been shown by earlier studies (e.g. Zerefos, 1983; Baldwin et al., 2001).

3.3 Correlation with ENSO

Apart from the QBO, which affects the variability of total ozone in the tropics, an important mode of natural climate variability in the tropics is the ENSO. To examine the impact of the ENSO on total ozone in the tropics we first removed variability related to the QBO and the solar cycle and then performed the correlation analysis with the SOI. The effect of the QBO was removed from the time series by using a linear regression model for the total ozone variations at each grid box, of the form

where D(t) is the monthly deseasonalized total ozone and t is the time in months, with t=0 corresponding to the initial month and t=T corresponding to the last month. The term a0 is the intercept of the statistical model. To model the QBO we made use of the equatorial zonal winds at 30 hPa. The term a1 is the regression coefficient of the QBO. The QBO component was removed from the time series by using a phase lag with a maximum correlation of 28 months (month lag −14 to month lag 13). The QBO-related coefficients α0 and α1 of Eq. (1) for the deseasonalized GOME-2A, GTO-ECV, TOMS–OMI–OMPS, and Oslo CTM3 zonal mean data are presented in Table 3. Additional information for the regression coefficients α1 of QBO is provided in the Supplement Fig. S1, which shows the spatial distribution of the regression coefficients in latitude–longitude maps.

The residuals from Eq. (1) were then inserted in a second regression (Eq. 2) to account for the effect of the solar cycle on total ozone, as follows:

where β0 and β1 are now the intercept and regression coefficients of the solar cycle, respectively. To model the solar cycle we used the 10.7 cm wavelength solar radio flux (F10.7) as a proxy, taken from the National Research Council and Natural Resources Canada at ftp://ftp.geolab.nrcan.gc.ca/data/solar_flux/monthly_averages/solflux_monthly_average.txt (last access: 12 Decembe 2018). We use the absolute solar fluxes, which are adjusted to account for variations in Earth–Sun distance and uncertainty in antenna gain and waves reflected from the ground. Latitude–longitude maps of the regression coefficients β1 of the solar cycle are presented in the Fig. S2. We note that the global pattern of the regression coefficients of the solar cycle from GOME-2A data matches well with what has been shown by Knibbe et al. (2014) with the reanalysis MSR data.

The remainders from Eq. (2) were used in a third regression (Eq. 3) to study the correlations between total ozone and SOI at each individual grid box:

where c0 and c1 are now the intercept and regression coefficients of ENSO, respectively. Estimates of the regression coefficients c1 are shown in the Fig. S3.

Figure 7Map of correlation coefficients between total ozone and SOI for GOME-2A (a), GTO-ECV (b), TOMS–OMI–OMPS satellite data (c), and Oslo CTM3 model simulations (d). Rectangles correspond to the southern Pacific region (10–20^∘ S, 180–220^∘ E) and southern Asia region (35–45^∘ N, 45–125^∘ E), blue cross corresponds to the Samoa station (14.25^∘ S, 189.4^∘ E), and red triangles correspond to stations in southern Asia, where total ozone has been studied as to the impact of ENSO after removing variability related to the annual cycle, QBO, and solar cycle. Positive correlations are shown in red colours, while negative correlations are shown in blue colours. Only correlation coefficients above or below ±0.2 are shown.

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Figure 7 presents the correlations between the SOI and total ozone from GOME-2A (panel a), GTO-ECV (panel b), and TOMS–OMI–OMPS satellite data (panel c) as well as between the SOI and the Oslo model simulations (panel d). All four plots refer to the period 2007–2016. As can be seen from Fig. 7a, correlations of > 0.3 between GOME-2A total ozone and the SOI are found in the tropical Pacific Ocean at latitudes between 25^∘ N and 25^∘ S. These correlations were tested as to their statistical significance in the period 2007–2016, using the t test for R with N−2 degrees of freedom (as in Zerefos et al., 2018), and were found to be statistically significant. A similar picture of correlation coefficients is also observed by the GTO-ECV and TOMS–OMI–OMPS data. Both datasets show similar results as to the range of correlations (> 0.3) in the tropical Pacific for the common period of observations. Nevertheless, the spatial resolution is higher in the GOME-2A and GTO-ECV ($\mathrm{1}\times \mathrm{1}{}^{\circ }$) data than in the TOMS–OMI–OMPS ($\mathrm{5}\times \mathrm{5}{}^{\circ }$) data, so the former datasets perform better when looking at smaller space scales. We have to note here that in both maps there are larger areas with correlation coefficients > 0.3 in the southern part of the tropics than in the northern part. However, this was mostly observed during the period 2007–2016. By examining the longer-term data record of the TOMS–OMI–OMPS data, which extends back to 1979, we find symmetry in the pattern of correlations north and south of the Equator in the tropical Pacific Ocean (Fig. A1 of Appendix A), which indicates that both sides of the tropical Pacific are affected more or less in a similar way by El Niño–La Niña events. Finally, the Oslo CTM3 gives small correlations (< 0.3) in the tropical Pacific Ocean around the Equator, except over the northern and southern subtropics where the model compares better with the observations.

Table 4Annual mean total ozone, amplitude of annual cycle, amplitude of QBO, amplitude of solar cycle, and amplitude of ENSO in the period 1995–2016 from GOME-2A, GTO-ECV, the combined TOMS–OMI–OMPS satellite data, and Oslo CTM3 model calculations over the southern Pacific region (10–20^∘ S, 180–220^∘ E) and at the Samoa station (14.25^∘ S, 189.4^∘ E), located within this region.

^* Period 2007–2016.

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The small rectangle in Fig. 7 corresponds to the southern Pacific region (10–20^∘ S, 180–220^∘ E), and the blue cross corresponds to the Samoa station (American Samoa; 14.25^∘ S, 189.4^∘ E), where total ozone has been studied with respect to the impact of ENSO after removing the variability related to the annual cycle, QBO, and the solar cycle. Figure 8 shows an example of the ENSO impact on total ozone in the southern Pacific Ocean. Figure 8a shows the time series of total ozone anomalies from GOME-2A, GTO-ECV, and TOMS–OMI–OMPS satellite data together with the SOI. Comparisons of GOME-2A data with GTO-ECV data, SBUV overpass data, and GB measurements at the Samoa station are shown in Fig. 8b. The dotted line shows the respective tropopause pressure anomalies from the NCEP reanalysis. All datasets point to the strong influence of ENSO on total ozone. Most evident is the strong decrease of about 4 % in 1997–1998, which was caused by the strongest El Niño event in the examined period. A strong decrease is also observed in the tropopause pressures by NCEP. Also notable is the strong La Niña event in 2010 which caused total ozone to increase by about 4 %. We calculate a strong correlation between total ozone from GTO-ECV and the SOI of +0.66 (99 % confidence level), which accounts for about 40 % of the variability of total ozone over the tropical Pacific Ocean when the annual cycle, QBO signal, and solar cycle are removed. From the regression with SOI we estimated an ENSO-related term from which we calculated the amplitude of ENSO in total ozone as (maximum ozone – minimum ozone)/2. The amplitude of ENSO in total ozone was estimated to be 8.8 DU, or 3.5 % of the annual mean. This is comparable to the amplitude of the annual cycle (7.7 DU, or 3.0 % of the mean) and is larger than the amplitude of QBO (2.2 DU or 0.8 % of the mean) and the amplitude of the solar cycle in this region (4.1 DU, or 1.6 % of the mean). These results are based on the GTO-ECV total ozone data. Similar results were also found at the Samoa station from GB observations (i.e. correlation with SOI: +0.55; amplitude of ENSO: 7.7 DU, or 3.0 % of the mean; amplitude of the annual cycle: 6.7 DU, or 2.7 % of the mean). Statistics of total ozone such as mean, amplitude of the annual cycle, amplitude of the QBO, amplitude of the solar cycle, and amplitude of the ENSO in total ozone over the selected areas are presented in Table 4. Satellite, GB, and model data show consistent results. It also appears that the Samoa station represents the greater area in the southern Pacific well as to the impact of the ENSO.

Figure 8(a) Example of regional time series of total ozone (%) over the southern Pacific region (10–20^∘ S, 180–220^∘ E) along with SOI. The dotted line shows the respective tropopause pressure variability from NCEP. R is the correlation coefficient between GTO-ECV total ozone and SOI (statistical significance of R is given in parentheses). The difference refers to the mean difference ±σ (in %) between GTO-ECV and the combined TOMS–OMI–OMPS satellite data; (b) same as in (a), but for SBUV overpass and GB data at the Samoa station. The difference refers to the mean difference ±σ (in %) between GTO-ECV and GB data.

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Differences between GOME-2A and its data pairs in the southern Pacific Ocean are of the order of $-\mathrm{0.2}\pm \mathrm{1.0}$ % between GOME-2A and TOMS–OMI–OMPS data, $-\mathrm{0.3}\pm \mathrm{0.9}$ % between GOME-2A and GTO-ECV, and $-\mathrm{0.9}\pm \mathrm{1.8}$ % between GOME-2A and Oslo CTM3. Accordingly, differences in Samoa are $-\mathrm{0.6}\pm \mathrm{1.9}$ % between GOME-2A and GB data, 0.0±1.4 % between GOME-2A and GTO-ECV, and $-\mathrm{0.1}\pm \mathrm{1.3}$ % between GOME-2A and SBUV. Despite the small differences found, we note here that GOME-2A values in the last 4 years of Figs. 8 and 9 slightly deviate from the other datasets and correlate weaker with the SOI than the other years in the time series. For instance, we estimate a drop in the correlation coefficient between GOME-2A and the SOI at the Samoa station (+0.58 in the period 2007–2012 and +0.47 in the period 2007–2016), which nevertheless does not alter the statistical significance of the correlation.

From Fig. 8 it also appears that there are high correlations with the tropopause height. The correlation coefficient between the NCEP tropopause pressure and GOME-2A total ozone over the southern Pacific Ocean is of the order of +0.59 (Student's t-test statistic results: t value =7.946, p value < 0.0001, and N=119). Accordingly, the correlation with GTO-ECV ozone data is of the order of +0.64 (t value =13.165, p value < 0.0001, and N=252), and with TOMS–OMI–OMPS, it is of the order of +0.58 (t value =10.913, p value < 0.0001, and N=241). The high correlation between the tropopause pressure and total ozone on interannual and longer time scales points to the very strong link between these parameters. These links were already documented in the past (e.g. Steinbrecht et al., 1998, 2001) and are verified with the GOME-2A data. At the same time a strong correlation is also evident between tropopause pressure and the SOI, again on interannual and longer time scales ($R=+\mathrm{0.66}$, t value =13.825, p value < 0.0001, N=252). The above results point to the strong impact of the ENSO on the tropical ozone column through the tropical tropopause; warm (El Niño) and cold (La Niña) events affect the variability of the tropopause, which in turn affects the distribution of stratospheric ozone. In the tropics, where total ozone is mainly stratospheric, as the tropopause moves to higher altitudes (lower pressure), the stratosphere is compressed, reducing the amount of stratospheric (total) ozone. This happens during warm (El Niño) episodes. The opposite phenomenon occurs during cold (La Niña) events, when the tropopause height decreases (higher pressure) and total ozone is then increased. These events can affect the long-term ozone trends in the tropics when looking at time periods when strong El Niño and La Niña events occur at the beginning and the end of the trend period respectively (Coldewey-Egbers et al., 2014).

Table 5Statistics of the comparisons between total ozone, tropopause pressures, and SOI for (a) southern Pacific (10–20^∘ S, 180–220^∘ E), (b) Samoa station (14.25^∘ S, 189.4^∘ E), (c) southern Asia (35–45^∘ N, 45–125^∘ E), and (d) seven stations in southern Asia.

^* The slope is in % per unit change of the explanatory variable. Error, t value, and p value refer to slope.

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Figure 9(a) Example of regional time series of total ozone (%) over southern Asia (35–45^∘ N, 45–125^∘ E) along with SOI. The dotted line shows the respective tropopause pressure variability from NCEP. R is the correlation coefficient between GTO-ECV total ozone and SOI (statistical significance of R is given in parentheses). The difference refers to the mean difference ±σ (in %) between GTO-ECV and the combined TOMS–OMI–OMPS satellite data; (b) same as in (a) but with SBUV overpass and GB data averaged at seven stations in southern Asia. The difference refers to the mean difference ±σ (in %) between GTO-ECV and GB data.

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Furthermore, in Fig. 8 we have marked seven stations in the greater southern Asia region (35–45^∘ N, 45–125^∘ E), where total ozone is anti-correlated with the SOI. Admittedly, these anti-correlations are weak (about −0.3), but we thought presenting the time series in these areas to be worthwhile as well. Figure 9 shows the variability of total ozone after removing seasonal, QBO, and solar-cycle-related variations, over the southern Asian region (panel a) and over the seven stations averaged within this region (panel b). As can be seen from this figure, the explained variance from the ENSO is small, not exceeding 9 %. All correlations from the comparisons with the SOI are summarized in Table 5. In spite of the small correlations with the SOI, the consistency between GOME-2A, GTO-ECV, TOMS–OMI–OMPS, and Oslo CTM3 data anomalies is very high, and their differences are within ±1 %. Differences at the seven stations in southern Asia are as follows: $-\mathrm{1.3}\pm \mathrm{2.4}$ % between GOME-2A and GB data, $-\mathrm{0.4}\pm \mathrm{1.0}$ % between GOME-2A and GTO-ECV, and $-\mathrm{0.5}\pm \mathrm{1.0}$ % between GOME-2A and SBUV.

Figure 10Map of correlation coefficients between total ozone and the NAO index during winter (December, January, and February; DJF) for GOME-2A (a), GTO-ECV (b), TOMS–OMI–OMPS satellite data (c), and Oslo CTM3 model simulations (d). Rectangles correspond to regions in the North Atlantic (35–50^∘ N, 20–50^∘ W; 15–27^∘ N, 30–60^∘ W), and red and green crosses correspond to stations in Canada and USA and Europe, where total ozone has been studied as to the impact of NAO after removing variability related to the annual cycle, QBO, solar cycle, and ENSO. Positive correlations are shown by red colours, while negative correlations are shown by blue colours. Only correlation coefficients above or below ±0.2 are shown.

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In summary, our findings indicate that GOME-2A captures the disturbances in total ozone during ENSO events well with respect to satellite SBUV and GB observations. Our findings on the ENSO-related total ozone variations (low ozone during ENSO warm events, high ozone during ENSO cold events, and magnitude of changes) are in line with recent studies (e.g. Randel and Thompson, 2011; Oman et al., 2013; Sioris et al., 2014) included in the 2014 Ozone Assessment report (Pawson and Steinbrecht, 2014; WMO, 2014). Our results are also in agreement with Knibbe et al. (2014), who showed negative ozone effects of El Niño between 25^∘ S and 25^∘ N, especially over the Pacific.

3.4 Correlation with NAO

The residuals from Eq. (3), free from seasonal, QBO, solar, and ENSO-related variations, were later used to study the correlation between total ozone and the NAO in winter. The results are presented in Fig. 10 which shows the correlation coefficients between total ozone and the NAO index in winter from the GOME-2A (panel a), GTO-ECV (panel b) and TOMS–OMI–OMPS satellite data (panel c), and the Oslo CTM3 model calculations (panel d). Negative correlations between total ozone and the NAO are presented with blue colours, while positive correlations are presented with red colours. From Fig. 10a it appears that total ozone is strongly correlated with the NAO in many regions. Strong negative correlation coefficients are observed in the majority of the northern mid-latitudes (R about −0.6), while positive correlations exist in the tropics and some negative correlations exist in the southern mid-latitudes. These characteristics are observed in both GTO-ECV and TOMS–OMI–OMPS datasets and are reproduced by the Oslo model as well, all for the common period 2007–2016. The regression coefficients on these comparisons are presented in the Fig. S4.

We note here that the results of the correlation analysis for the period 2007–2016 were based on a relative small sample of data from 10 winters, therefore many of these correlation coefficients may not be statistically significant. The statistical significance of the correlation coefficients in every grid box was only tested with the TOMS–OMI–OMPS data (Fig. A2, Appendix A), which provided us with the opportunity to calculate the respective correlations using data for the whole period of record 1979–2016. It appears that when extending the data back to the 1980s, the negative correlations in the southern mid-latitudes in winter disappear while the positive correlations in the tropics become weaker; yet the observed anti-correlation between total ozone and the NAO index in the northern mid-latitude zone remains strong. The dotted line in the plot shows areas with statistically significant correlation coefficients (99 % confidence level). Indeed, in the long term, statistically significant correlations between total ozone and the NAO index during winter are mostly found over the northern mid-latitudes and the subtropics. A small, statistically significant signal is also seen over Antarctica, but it was not analysed further.

According to this finding, we have restricted the analysis of the NAO to the northern mid-latitudes. Rectangles (Fig. 10a) correspond to two regions in the North Atlantic, i.e. 35–50^∘ N, 20–50^∘ W and 15–27^∘ N, 30–60^∘ W, which were studied for the impact of the NAO on total ozone after removing variability related to the annual cycle, QBO, solar cycle, and ENSO. In addition we have studied a number of stations in Canada, USA, and Europe that contribute ozone data to WOUDC, which are marked by red and green crosses in Fig. 10. The red crosses refer to the monitoring stations in Canada and the US, and the green crosses refer to the stations in Europe. In Fig. 11 we present the times series of total ozone anomalies from GOME-2A, GTO-ECV, and TOMS–OMI–OMPS satellite data along with the NAO index in winter over the North Atlantic. Model calculations are shown as well. The dotted line shows the respective tropopause pressure anomalies from NCEP reanalysis. Comparisons between GOME-2A, GTO-ECV, SBUV (v8.6) overpass data, and GB measurements over the various stations in Canada, USA, and Europe are shown in Fig. 12.

The observed anomalies over the North Atlantic Ocean point to the strong influence of the NAO on total ozone in winter. Most evident is the strong increase in total ozone in 2010 of more than 8 %, particularly over 35–50^∘ N and 20–50^∘ W. This increase was accompanied by a strong increase in tropopause pressures. Both changes (in total ozone and tropopause pressures) occurred under a strong negative phase of the NAO, the strongest one in the past 20 years. We observe strong anti-correlation among total ozone and the NAO index in winter ($R=-\mathrm{0.74}$ over 35–50^∘ N, 20–50^∘ W), which is statistically significant at the 99 % confidence level. This anti-correlation suggests that about 50 % of the variability of total ozone in winter is explained by the NAO when the annual cycle, QBO, solar cycle, and ENSO signals are removed. Differences for GOME-2A, and its data pairs are estimated to be $-\mathrm{0.7}\pm \mathrm{1.1}$ % between GOME-2A and TOMS–OMI–OMPS data, $+\mathrm{0.1}\pm \mathrm{1.0}$ % between GOME-2A and GTO-ECV, and $-\mathrm{0.2}\pm \mathrm{1.5}$ % between GOME-2A and Oslo CTM3 data. From the regression with the NAO index we derived an NAO-related term from which we calculated the amplitude of the NAO in total ozone as (maximum ozone – minimum ozone)/2. The amplitude of the NAO over the North Atlantic region (35–50^∘ N, 20–50^∘ W) was estimated to be about 16.5 DU, or 5.2 % of the annual mean. This is about half of the amplitude of the annual cycle (which is ∼37 DU or 11.7 % of the mean). These estimates are based on GTO-ECV data. Similar correlation and amplitude were also found with GOME-2A, the combined TOMS–OMI–OMPS satellite data, and the Oslo CTM3 model simulations.

Table 6Annual mean total ozone, amplitude of annual cycle, amplitude of QBO, amplitude of solar cycle, and amplitude of NAO in the period 1995–2016 from GOME-2A, GTO-ECV, the combined TOMS–OMI–OMPS satellite data, and Oslo CTM3 model calculations over the North Atlantic Ocean, in (a) region 35–50^∘ N, 20–50^∘ W, and (b) region 15–27^∘ N, 30–60^∘ W.

^* Period 2007–2016.

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A similar but opposite correlation is found over the southern part of the North Atlantic (15–27^∘ N, 30–60^∘ W). Here, we estimate a significant correlation coefficient of the NAO of +0.60, amplitude of the NAO of about 7.2 DU (2.6 % of the annual mean), and amplitude of the annual cycle of about 15.8 DU (5.7 % of the mean). Again, similar estimates are found with the GOME-2A and the TOMS–OMI–OMPS satellite data and are reproduced by the model calculations as well. The annual mean total ozone and the amplitudes of the annual cycle, QBO, solar cycle, and NAO in total ozone over the studied regions in the North Atlantic are summarized in Table 6. Differences between GOME-2A and GTO-ECV data at the southern part of North Atlantic are of the order of $-\mathrm{0.6}\pm \mathrm{0.7}$ %. Differences with the TOMS–OMI–OMPS data are estimated to be $-\mathrm{0.9}\pm \mathrm{0.8}$ % and are estimated to be $-\mathrm{0.1}\pm \mathrm{0.7}$ % with the Oslo CTM3.

Figure 11Example of regional time series of total ozone (%) over the North Atlantic regions (a) 35–50^∘ N, 20–50^∘ W, and (b) 15–27^∘ N, 30–60^∘ W, in winter (DJF mean) along with the NAO index. The dotted line shows the respective tropopause pressure variability from NCEP reanalysis. R is the correlation coefficient between GTO-ECV total ozone and the NAO index. The differences refer to the mean differences ±σ (in %) between GTO-ECV and the combined TOMS–OMI–OMPS satellite data.

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The time series of total ozone anomalies and of the NAO index for the examined stations in Canada, USA, and Europe are presented in Fig. 12. Table 7 presents the respective statistics. The correlation between total ozone and the NAO index in winter after removing ozone variability related to the annual cycle, QBO, solar cycle, and ENSO is −0.40 (90 % confidence level). Again, a particular feature was the total ozone increase in 2010 by 6 % of the mean associated with the negative NAO phase. This increase is noteworthy because of the consistency with the GB measurements and the satellite SBUV overpass data and, in general, the agreement found between the variability of the tropopause pressures and total ozone. Differences between GOME-2A and GB data are $-\mathrm{1.0}\pm \mathrm{1.8}$ %. Accordingly we estimate differences of about $-\mathrm{1.1}\pm \mathrm{0.5}$ % between GOME-2A and GTO-ECV data and of about $-\mathrm{1.3}\pm \mathrm{0.6}$ % between GOME-2A and SBUV data. On the basis of GTO-ECV data we estimate that in Canada and the USA, the amplitude of the NAO in total ozone in winter is about 7 DU (or 2.2 % of the mean), while it is estimated to be about 9 DU (or 2.7 % of the mean) over Europe. These numbers are slightly smaller than the GOME-2A, GB, and SBUV estimates, less than about one percent (Table 7).

Table 7Annual mean total ozone, amplitude of annual cycle, amplitude of QBO, amplitude of solar cycle, and amplitude of NAO in the period 1995–2016 from GOME-2A, GTO-ECV satellite data, ground-based observations, and SBUV (v8.6) satellite overpass data over (a) Canada and USA (11 station mean) and (b) Europe (41 station mean).

^* Period 2007–2016.

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Table 8Statistics of the comparisons between total ozone, tropopause pressures, and NAO index in winter (DJF mean) for (a) the northern part of North Atlantic (35–50^∘ N, 20–50^∘ W), (b) its southern part (15–27^∘ N, 30–60^∘ W), (c) 11 stations in Canada and USA, and (d) 41 stations in Europe.

^* The slope is in % per unit change of the explanatory variable. Error, t value, and p value refer to slope.

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The anti-correlation between total ozone column and the NAO index during winter also applies to southern Europe and the Mediterranean. Following the study of Ossó et al. (2011), who reported a reversal in the correlation pattern between the NAO and total ozone from winter to summer in southern Europe, we have looked at the correlations during summer as well. Figure 13 presents the comparisons for 21 ground-based stations located in the region bounded by latitudes 30–47^∘ N and by longitudes 10^∘ W–40^∘ E. Figure 13a shows results for the summer, and Fig. 13b shows results for the winter. As can be seen, the observed anti-correlation between GB total ozone and the NAO in winter ($R=-\mathrm{0.43}$, slope $=-\mathrm{0.980}$, t value $=-\mathrm{2.095}$, p value =0.0499, and N=21) reverses its sign and becomes positive in the summer ($R=+\mathrm{0.60}$, slope =0.874, t value =3.309, p value =0.0037, and N=21), indicating that the NAO explains about 36 % of ozone variability in the summer in this region. A similar picture is also seen from GOME-2A, GTO-ECV, and SBUV data.

Figure 12Comparison with GB observations over (a) Canada and USA and (b) Europe in winter (DJF mean). R is the correlation coefficient between GTO-ECV total ozone and the NAO index. The differences refer to the mean differences ±σ (in %) between GTO-ECV and GB data.

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Figure 13Relation between total ozone and the NAO index in summer (JJA mean) and winter (DJF mean) for 21 stations in southern Europe. The correlation coefficients refer to NAO index and GB total ozone after removing variability related to the seasonal cycle, QBO, solar cycle, and ENSO.

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In summary, our findings based on GOME-2A, GTO-ECV, and SBUV overpass data are in line with those found by Ossó et al. (2011) and Steinbrecht et al. (2011), who analysed TOMS and OMI satellite data and GB measurements at the Hohenpeißenberg station, respectively. During winter, total ozone variability associated with the NAO is particularly important over northern Europe, the US East Coast, and Canada, explaining up to 30 % of total ozone variance for this region (Ossó et al., 2011). Also, both studies found unusually high total ozone columns in 2010 over much of the Northern Hemisphere and related them to the negative phase of the NAO or AO (the Arctic Oscillation).