Statistical analysis of total ozone measurements in Oslo, Norway,

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi: /2004jd004679, 2004 Statistical analysis of total ozone measurements in Oslo, Norway, T. M. Svendby and A. Dahlback Department of Physics, University of Oslo, Oslo, Norway Received 23 February 2004; revised 4 June 2004; accepted 10 June 2004; published 31 August [1] The total ozone series from Dobson spectrophotometer 56 (D56) operating at the University of Oslo (59.9 N, 10.7 E), Norway, from March 1978 to May 1998 has recently been reevaluated. Ozone trends for the time series are calculated using multiple regression models with explanatory variables for the 11 year solar cycle (lagged 22 months), the 2.5 year Quasi-Biennial Oscillation (QBO) cycle, stratospheric aerosol loading from volcanoes, detrended temperatures at 100 hpa and 500 hpa levels, El Niño Southern Oscillation (ENSO) events, and various teleconnection patterns. Six of the teleconnection explanatory variables examined are significant to a 95% confidence level in describing the observed total ozone variations. The year-round total ozone trend in Oslo (±SD) is estimated to 4.2 ± 0.4 % per decade, whereas the winter and summer trends amount to 6.2 ± 0.9 % per decade and 2.4 ± 0.5 % per decade, respectively. The analyses imply that climatic and dynamical changes account for up to a 20% increase in winter ozone trend and a 40% decrease in summer ozone trend. The East Atlantic Jet is the single most dominating teleconnection pattern in Oslo and contributes to about 0.7 percentage points per decade change in summer total ozone trend. The regression analyses demonstrate that the start and termination dates of the ozone record are highly important for the trend results: The summer and winter ozone trend can change by 0.9 percentage points per decade if 1 year is included or omitted from the analyses. INDEX TERMS: 0340 Atmospheric Composition and Structure: Middle atmosphere composition and chemistry; 0341 Atmospheric Composition and Structure: Middle atmosphere constituent transport and chemistry (3334); 0370 Atmospheric Composition and Structure: Volcanic effects (8409); 1610 Global Change: Atmosphere (0315, 0325); KEYWORDS: ozone, stratosphere, trend, regression analysis, teleconnection indices Citation: Svendby, T. M., and A. Dahlback (2004), Statistical analysis of total ozone measurements in Oslo, Norway, , J. Geophys. Res., 109,, doi: /2004jd Introduction [2] A number of studies in recent years, based on satellite data from TOMS and ground-based Dobson measurements of total ozone, have shown a significant negative ozone trend over middle and high latitudes in the Northern Hemisphere [e.g., Stolarski et al., 1992; Fioletov et al., 2002; World Meteorological Organization (WMO), 2003]. The ozone losses are related not only to increased stratospheric chlorine and bromine loading, but also to changes in tropospheric and stratospheric temperatures at middle and high latitudes [WMO, 1999]. Linked to the ozone decrease is an increase of the UV-B solar radiation intensity reaching the ground [Zerefos, 2002]. [3] The most commonly used tools for trend determination of stratospheric ozone measurements are multiple linear regression models [Bojkov et al., 1990; WMO, 1999], since they allow for description of meteorological influence, the 11 year solar cycle and volcanic aerosols. In this work we have used such a regression model to study the total ozone trend in Oslo for the period March 1978 to May Copyright 2004 by the American Geophysical Union /04/2004JD During this 20 year period, systematic ozone registrations were performed with Dobson spectrophotometer 56 (D56). The data set has recently been reevaluated and corrected for interference from SO 2 pollution in the atmosphere [Svendby and Dahlback, 2002]. [4] In June 1998 the D56 instrument was moved from Oslo to Harestua (40 km outside Oslo), and Brewer spectrophotometer 42 replaced D56 as the primary ground-based total ozone registration instrument at the University of Oslo. Owing to the systematic differences between Dobson and Brewer total ozone measurements [Kerr et al., 1988; Staehelin et al., 1998; Svendby and Dahlback, 2002], we have based our ozone trend analysis entirely on the Dobson series from 1978 to [5] In addition to studies of the 20 year total ozone trend in Oslo, we have examined how the various explanatory variables correlate with the total ozone variations. 2. Model Description 2.1. Regression Model [6] Total ozone trends in Oslo are calculated by a multiple regression model which is commonly used for 1of11

2 trend determination of atmospheric ozone [e.g., Bojkov et al., 1990; Staehelin et al., 1998; WMO, 1999; Harris et al., 2001]: y t ¼ X12 i¼1 m i I i;t þ X12 i¼1 ðb i t þ C i ÞI i;t þ Xk g n Z n;t þ N t ; n¼1 where y t measured total ozone monthly mean of month t; t index of the month counted from the beginning of the measuring period (t = 1,2,...245); i index of calendar month in the year (i.e., i = 1 for January, i = 12 for December); m i ozone mean in month i (average values for the period); I i,t indicator series for the ith month of the year (I i,t =1if t corresponds to the ith month of the year, otherwise 0); b i trend of the ith month of the year (DU/month); C i constant term of month i, taking into account that the trend started prior to 1978; Z n,t explanatory variable (monthly mean values), n = 1,2,..., k; g n coefficient corresponding to explanatory variable Z n,t ; N t residual noise series. Explanatory variables and coefficients used were Z 1,t Quasi-Biennial Oscillation (QBO), g 1 is the associated coefficient; Z 2,t solar 10.7 cm radio flux (F10.7), g 2 is the associated coefficient; Z 3,t aerosol optical depth (AOD), g 3 is the Z 4,t Z 5,t : Z 6,t to Z k,t ð1þ associated coefficient; stratospheric and/or tropospheric temperature (T100, T300 or T500), g 4 is the associated coefficient; El Niño Southern Oscillation (ENSO), g 5 is the associated coefficient; various teleconnection patterns, g 6 to g k are the associated coefficients. [7] Initial model test runs reveal that a month-to-month correlation exists in the noise term. The correlation problem is solved by using the autoregressive AR(1) model, i.e., allowing N t to depend on its previous value: N t ¼ jn t 1 þ e t ; where j is the coefficient associated with N t 1 and e t is the uncorrelated residual describing the fit between observed and modeled monthly mean total ozone values. [8] It is well known that ozone is more variable in winter/ spring than during the summer/fall. Regression analyses, however, require that variations in ozone are approximately the same for all months. This problem is solved by statistical weighting of the observations. The monthly variance of e t is estimated during statistical runs, secondly the weights 1/var i are applied to the model. When introducing weights the coefficients b 1 12, C 1 12, g 1 k, and j are estimated from the matrix equation ðx t WXÞ 1 X t WY; where X is the design matrix comprising explanatory variable indices for the 245 months studied, Y is a single column matrix with monthly ozone observations, and W is a matrix with monthly weights along its diagonal. The weighting entails a slight increase in the uncertainty estimates of winter/spring trends and a slight decrease of the summer/fall trends. [9] The overall goal of the regression analyses is to determine the total ozone trend in Oslo and to evaluate the influence of the explanatory variables. For each model run we perform a t-test to examine the null hypothesis g n = 0, versus the alternative hypothesis g n 6¼ 0. As a thumb of rule we consider an explanatory variable as significant in describing ozone variations if P < 0.05, i.e., the probability P of making a mistake by rejecting the null hypothesis is less than 5% Explanatory Variables [10] Regression analyses from equation (1) can be used with or without inclusion of explanatory variables. However, the seasonality in the ozone mean level (m i ) has to be accounted for in all trend analyses. For the Oslo station the seasonal means (shown in Figure 1) are calculated from the reevaluated and SO 2 corrected D56 data set from March 1978 to May The monthly D56 total ozone values from Oslo (shown in Figure 2a) are available at the World Ozone and Ultraviolet Radiation Data Center ( In the following, a brief discussion of the most relevant explanatory variables is given Quasi-Biennial Oscillation (QBO) [11] Several studies have shown that ozone is dependent on the stratospheric circulation changes related to the Quasi- Biennial Oscillation (QBO) [e.g., WMO, 1999; Steinbrecht et al., 2001; Fioletov and Shepherd, 2003]. Several representations of QBO are possible, from simply using a single level of Singapore wind data, to more sophisticated methods describing QBO as a function [Ziemke et al., 1997; Wallace et al., 1993]. For high northern latitudes, however, the advanced QBO representations only introduce minor improvements to the statistical models, meaning that the root mean squares of residuals are not reduced significantly [Ziemke et al., 1997]. Given that our main focus is on trend analyses, we have chosen the simplest method and used the 50 hpa Singapore wind as a QBO index (see Figure 2b). Initial statistical analyses based on equation (1) (with QBO, AOD, F10.7 and temperature data as explanatory variables) are performed to examine the influence of QBO on total ozone for various lags, i.e., months of delay giving the strongest ozone/qbo correlation. Lags of 0 and 1 months result in the lowest P values (largest negative correlation between QBO and ozone variations) whereas lag 16 months gives the largest positive correlation (with slightly higher P value than for lag 0). Similar results have been reported by Bojkov et al. [1990] and by the NASA/WMO Ozone Trend Panel. In our final analyses of total ozone trends we have used the nonlagged QBO series in the regression model Solar 10.7 cm Radio Flux (F10.7) [12] The 11 year cycle of the solar flux can also have a measurable influence on the ozone concentration [WMO, 2003]. In our study we have used the 10.7 cm radio flux from Ottawa and Pentiction, Canada, as manifestations of solar activity (data from 2of11

3 Figure 1. Seasonal total ozone mean calculated from the D56 data set for the period March 1978 to May cnrc.gc.ca/icarus/www/maver.txt). The data are plotted in Figure 2c. Initial statistical test runs based on equation (1) were performed to examine the dependence of modeled ozone variations on time lag in solar flux. The test runs show that the solar correlation with ozone is most significant for lags of 12, 22 or 33 months, with the 22 month lag giving the lowest P value. Figure 3 illustrates the results from the test runs, with P values plotted as function of lag. The strong correlation between ozone and F10.7 at 22 months lag makes this delay a natural choice in the final trend analyses. [13] The physical mechanism explaining the solar influence on the atmosphere is still not fully understood, but simulations from Tourpali et al. [2003] suggest that solar flux variations might affect the North Atlantic Oscillation. Further, Soukharev and Hood [2001] suggest that there is a possible solar modulation of QBO and they have demonstrated that the wind data in the lower stratosphere (50 hpa) is months lagged relative to the F10.7 solar flux. Also, Willett [1962] found peak correlation at a lag of 1 1 = 2 to 2 years of the sunspot cycle relative to total ozone. Finally, regression analyses from Staehelin et al. [1998] reveal a maximum ozone/solar flux correlation at 32 months time lag, coinciding with one of the local correlation maxima found in our studies Aerosol Optical Depth (AOD) [14] In the 1980s and 1990s there were two major eruptions producing considerable amounts of stratospheric aerosols: El Chichon (1982) and Pinatubo (1991). These eruptions had a significant influence on the total ozone amount in the stratosphere [Robock, 2002]. Time series of aerosol optical depth (AOD), based on monthly zonal mean values at 550 nm, is downloaded from NASA Goddard Institute for Space Studies ( strataer/strataer.txt). In our regression model, AOD zonal mean data from 58 N is used, illustrated in Figure 2d. Initial statistical test runs based on equation (1) show that the correlation between ozone variations and AOD is strongest for zero time lag. The P values increase gradually as the lag increases Temperature [15] The day-to-day variations in total ozone is strongly related to stratospheric and tropospheric temperatures [Dobson, 1929]. In order to study the correlation between temperature and total ozone variations we have used monthly mean radiosonde temperature data from Gardermoen (47 km north of Oslo), provided by the Norwegian Meteorological Institute. Temperature data from three different atmospheric levels (identified by pressure) have been selected: 100 hpa, 300 hpa, and 500 hpa levels, named T100, T300, and T500, respectively. The deseasonalized temperature data for the 100 hpa level is shown in Figure 2e. There has been a gradual temperature decrease in the 100 hpa region from 1978 to 1998, partly linked to depletion of the ozone layer [WMO, 1999]. For the 300 hpa and 500 hpa temperature levels, however, temperature changes will mainly originate from climatic or circulation changes in the atmosphere. Figure 4 shows monthly temperature trend (percent/year) from 1978 to 1998 measured at Gardermoen. On average the temperatures at the 300 hpa and 500 hpa levels have increased during the winter months, whereas temperature decease is observed for most summer months. To remove possible effects of climatic changes and anthropogenic influence from the temperature data, we have detrended the 100 hpa, 300 hpa and 500 hpa data with respect to the monthly trend pattern shown in Figure El Niño Southern Oscillation (ENSO) [16] The El Niño Southern Oscillation (ENSO) is an episodic phenomenon, and its effect on total ozone depends both on location and season [Randel and Cobb, 1994]. Especially, major ENSO warm events have been suggested as partly responsible for the negative ozone anomalies in [Bojkov, 1987; WMO, 1999]. In our data analyses we have used the Southern Oscillation index (SO index), shown in Figure 2f, as a description of the ENSO event. The data are downloaded from gov/climateindices. The SO index describes the difference between the sea level pressure at Tahiti and Darwin, relative to the 1950 to 1993 reference period Teleconnection Patterns [17] Large-scale atmospheric circulation systems might influence the total ozone concentrations [Brönnimann et al., 2000; Appenzeller et al., 2000; Steinbrecht et al., 2001]. Consequently, we have tested the regression model with inclusion of indices for eight different teleconnection patterns, describing monthly mean anomaly heights at 500 hpa level [Bell and Halpert, 1995]. The teleconnection data described below are from the National Oceanic and Atmospheric Administration ( gov/data/teledoc/telecontents.html) and from Jones et al. [1997]. [18] One of the most well-known teleconnection pattern is the North Atlantic Oscillation (NAO). It consists of a north-south dipole of anomalies, with one pole located over Greenland and the other spanning the North Atlantic around 40 N. The East Atlantic pattern (EA) represents a second prominent mode of low-frequency variability over the North Atlantic, appearing in all months except May August. This pattern is structurally similar to the NAO, but the anomaly centers in the EA pattern are displaced southeastward compared to the approximate centers of the NAO pattern. Contrary to the EA, the East Atlantic Jet pattern (EA-jet) only appears between April and August. This pattern consists of a north-south dipole of anomaly centers, with one main center located around Scandinavia, and the other center located over northern Africa and the Mediterranean 3of11

4 Figure 2. Explanatory variables used in the regression model. (a) Deseasonalized time series of monthly mean total ozone in Oslo; (b) Quasi-Biennial Oscillation (QBO), monthly average 50 hpa Singapore wind; (c) monthly average 10.7 cm radio solar flux; (d) aerosol optical depth (AOD) at 550 nm; (e) deseasonalized 100 hpa temperatures measured at Gardermoen; (f ) ENSO represented by the Southern Oscillation index (SOI). Sea. A fourth teleconnection pattern is the East Atlantic/ West Russia pattern (EA/WR), also called Eurasia-2 by Barnston and Livezey [1987]. In winter, two main anomaly centers compose the EA/WR pattern, located over Western Europe and the Caspian Sea. The EA/WR is absent from June to August. Similar to EA/WR the Scandinavian (SCAND) pattern (or Eurasia-1 pattern) appears all year, except in June and July. It consists of a primary circulation center which spans Scandinavia/Arctic Ocean and two weaker centers over Western Europe and Western China. The sixth pattern used in the regression model is the Polar/ Eurasia pattern (POL). It only appears during the winter (December February) and consists of one main anomaly centre over the polar region, and separate centers of oppo- 4of11

5 Figure 3. P values for F10.7 solar flux as a function of time lag, describing the correlation between solar flux and total ozone in Oslo. The dashed line represents the 95% significance level. site sign over Europe and northeastern China. Also, the West Pacific pattern (WP) has proved to be significant in describing variations in total ozone. It has a primary action center over the North Pacific in all months, and one or two other centers with strong seasonal-dependent locations. Finally, we have performed statistical analyses with inclusion of the Pacific/North American pattern (PNA). It is one of the most prominent modes of low-frequency variability in the Northern Hemisphere extratropics, but might also affect the atmospheric circulation at higher latitudes. This pattern appears in all months except June and July. 3. Results 3.1. Regression Analyses [19] Total ozone trends in Oslo for the period March 1978 to May 1998 are calculated from equation (1) with various explanatory variables. The results from eight statistical runs are presented in Table 1. The fit between modeled and observed total ozone values is expressed by the R 2 value (square of the correlation coefficient, in %) and the standard deviation of residuals (SD res). As an indicator of the influence of the explanatory variables on total ozone, Table 1 comprises the coefficients g n and the P values of Z n (P is the probability of making a mistake by rejecting the null hypothesis g n = 0). The year-round trend, summer trend (May August), and winter trend (December March) for the reanalyzed data set are listed in the last column of Table 1. [20] The F10.7 solar flux series used in the statistical model is lagged 22 months, meaning that the solar maxima are more or less coincident with stratospheric aerosol maxima observed after the eruptions of El Chichon and Mt. Pinatubo, respectively. These simultaneous incidences of volcanic eruptions and lagged solar maxima disturb the analysis and might result in a misleading conclusion of insignificant solar flux influence (P = when F10.7 is used as the only explanatory variable). However, when volcanic aerosol optical depth (AOD) is included in the regression model along with F10.7 solar flux, both explanatory variables become highly significant with P < 10-3 (see run 2 in Table1). The aerosol optical depth data used in the analyses are divided into a summer season (May October) and a winter season (November April), named AOD s and AOD w, respectively. As demonstrated from run 2 the correlation between ozone and AOD is largest for the winter season. [21] Run 3 demonstrates that stratospheric circulation related to the summer values of Quasi-Biennial Oscillation (QBO s ) is a significant explanatory variable in the statistical model (P = 0.002), contrary to the winter values (QBO w ) with P values of QBO has a relatively short cycle of 2.5 years and the overall influence on the total ozone trend is limited. Comparison of runs 2 and 3 shows that summer and winter ozone trends in Oslo are increased by approximately 0.1 percentage points per decade (hereafter denoted %p/decade) when QBO is used as explanatory variable in addition to F10.7 solar flux and AOD. It should be mentioned that the trend results in run 2 and 3 are essentially unchanged when the seasonality in QBO and AOD is replaced by year-round values. [22] By studying the R 2 values and the standard deviation of the residuals (SD-res) it can clearly be seen that inclusion of the deseasonalized and detrended 100 hpa temperatures (T100) represents a major improvement to the statistical model. Whereas R 2 amounts to 86.6% in run 3 the value increases to 93.8% in run 4. The year-round trend is hardly affected by inclusion of T100 data in the model, but the standard deviations of the trend calculations are significantly reduced. The strong correlation between stratospheric ozone and 100 hpa temperature is not surprisingly, due to the fact that low 100 hpa temperatures normally are associated with high tropopause heights, giving low total ozone values [Steinbrecht et al., 2001]. [23] Comparison of runs 3 and 4 also demonstrates that the inclusion of 100 hpa temperature influences the importance of QBO, F10.7 and AOD. Even if F10.7 and AOD have P values below 10-3 in both model runs, the g n Figure 4. Temperature trend for the period 1978 to 1998 measured at Gardermoen. The solid, dotted, and dashed curves represent 100 hpa, 300 hpa, and 500 hpa temperatures, respectively. 5of11

6 Table 1. Results From Statistical Runs Calculated From Equation (1), Based on the Reevaluated and SO 2 Corrected D56 Total Ozone Series From Oslo a Run R 2,% (SD Res) Explanatory Ozone Trend ± SD, Variables Z n g n Coefficients ± SD P Value of Z n % per decade (17.5) None Y: 4.4 ± 0.6 S: 2.5 ± 0.9 W: 6.6 ± (16.6) F ± <10 3 Y: 4.0 ± 0.6 AOD s ; AOD w 164 ± 35; 236 ± 44 <10 3 ; <10 3 S: 2.3 ± 0.8 W: 6.0 ± (16.5) F ± <10 3 Y: 4.1 ± 0.6 AOD s ; AOD w 176 ± 34; 233 ± 44 <10 3 ; <10 3 S: 2.4 ± 0.8 QBO s ; QBO w 0.27 ± 0.08; 0.18 ± ; W: 6.1 ± (11.4) F ± <10 3 Y: 4.2 ± 0.4 AOD s ; AOD w 116 ± 24; 161 ± 30 <10 3 ; <10 3 S: 2.4 ± 0.5 QBO s ; QBO w 0.14 ± 0.06; 0.12 ± ; W: 6.2 ± 0.9 T ± 0.37 < (15.4) F ± <10 3 Y: 4.2 ± 0.5 AOD s ; AOD w 152 ± 32; 196 ± 41 <10 3 ; <10 3 S: 2.5 ± 0.7 QBO s ; QBO w 0.22 ± 0.08; 0.22 ± ; W: 6.2 ± 1.2 T ± 0.51 < (11.2) F ± <10 3 Y: 4.2 ± 0.4 AOD s ; AOD w 113 ± 24; 157 ± 30 <10 3 ; <10 3 S: 2.4 ± 0.5 QBO s ; QBO w 0.14 ± 0.06; 0.14 ± ; W: 6.2 ± 0.9 T ± 0.42 <10 3 T ± (14.4) F ± Y: 4.6 ± 0.5 AOD s ; AOD w 145 ± 32; 207 ± 42 <10 3 ; <10 3 S: 3.6 ± 0.7 QBO s ; QBO w 0.20 ± 0.08; 0.08 ± ; W: 6.2 ± 1.2 EA/WR 5.45 ± 1.20 <10 3 EA-jet 3.93 ± EA 3.89 ± WP 2.19 ± ENSO 0.19 ± PNA 2.37 ± NAO 1.15 ± SCAND 0.38 ± POL 0.57 ± (15.4) F ± <10 3 Y: 4.1 ± 0.6 AOD s ; AOD w 152 ± 32; 196 ± 41 <10 3 ; <10 3 S: 2.7 ± 0.7 QBO s ; QBO w 0.22 ± 0.08; 0.22 ± ; W: 5.3 ± 1.2 T500 b 3.21 ± 0.51 <10 3 a The F10.7 solar flux is lagged by 22 months, whereas the other explanatory variables are nonlagged. AOD and QBO are separated into summer and winter seasons, indicated by subscripts s and w, respectively. The last column represents year-round ozone trend (Y), summer ozone trend May Aug. (S), and winter ozone trend Dec. Mar. (W). b Not detrended temperature. coefficients are smaller for all explanatory variables in run 4 compared to run 3. The relationship between AOD and temperature is demonstrated in several studies [WMO, 1999; Robock, 2002; Beig et al., 2002] and is driven by heating effects associated with volcanic aerosols. Also, correlation between stratospheric temperature and F10.7 solar flux has been established [Nikolashkin et al., 2001] and so has correlation between ozone and F10.7 for various QBO phases [Soukharev, 1997]. [24] In run 5 ozone variations related to deseasonalized and detrended 500 hpa temperature are studied. Not surprisingly, the T500 explanatory variable is well correlated with the observed variations in total ozone (P < 10-3 ). However, the model fit in run 5 (R 2 = 88.8%) is considerably poorer than that obtained from run 4 (R 2 = 93.8%). While there is a positive correlation between ozone and T100, there is a negative correlation between ozone and T500. This correlation is well documented from other studies [e.g., Reinsel et al., 1981; Chandra et al., 1996]. The T300 explanatory variable has also been tested in the regression model, giving results very similar to the ones obtained from T500. However, the R 2 value becomes slightly lower when T300 replaces T500. [25] It should be mentioned that the estimated ozone trend is very sensitive to whether temperature data are detrended with respect to each month or with respect to the average annual temperature increase (or decrease). As shown in Figure 4 the temperature trend for the period changes significantly from one month to another. This is especially the case for T500. In fact, the modeled total ozone winter trend is reduced by 0.8 %p/decade if 500 hpa temperature is detrended with respect to the annual average 6of11

7 Figure 5. Residuals versus years of (left) run 1 and (right) run 6. temperature trend instead of the monthly values. This difference is strongly influenced by the high-temperature trend registered in January. [26] For the Oslo station the correlation between 100 hpa and 500 hpa temperature is about In spite of this temperature correlation, the inclusion of both T100 and T500 explanatory variables slightly improves the regression model and gives an R 2 value of 93.9% (compare runs 4 and 6). A similar pattern was found by Harris et al. [2001] who concluded that different processes could be accounted for by using temperatures at the two levels. The ozone residuals from run 6 are shown in Figure 5 (along with residuals from run 1). As we can see, the residual variance is reduced significantly when explanatory variables are applied in the regression model. [27] The statistical analyses described in runs 1 6 are done without inclusion of El Niño Southern Oscillation or any teleconnection patterns. It is well-known that largescale atmospheric circulation might influence total ozone [Brönnimann et al., 2000] and in run 7 the influence of ENSO (represented by the SO index) and eight teleconnection patterns are examined. Because of the strong correlation between atmospheric dynamics and tropospheric/ stratospheric temperature, T100 and T500 are excluded from the model. [28] Comparisons of run 7 and 3 demonstrate that the teleconnection patterns strongly influence the total ozone summer trend in Oslo. Totally, the inclusion of ENSO and teleconnection patterns give rise to an ozone summer trend increase of 1.2 %p/decade. The three indices with largest effect on total ozone variations are the East Atlantic/West Russia pattern (EA/WR), the East Atlantic jet (EA-jet), and the East Atlantic pattern (EA). All of them are significant to a 95% confidence level. The substantial influence of these patterns is in good agreement with analyses from Orsolini and Doblas-Reyes [2004]. The impact of the individual circulation indices on total ozone trend can be studied more detailed in Table 2, indicating that EA-jet gives the single largest contribution to the increased summer ozone trend (EA-jet alone is responsible for an ozone trend increase of 0.7 %p/decade). Studies of variations in winter ozone trend, however, show that the East Atlantic and East Atlantic/West Russia pattern are most dominating. Whereas EA/WR tends to decrease the winter trend by 0.3 %p/decade, the EA pattern increases the trend by a similar amount. Thus in total the winter ozone trend in Oslo is barely affected by the circulation anomalies. [29] In addition to EA/WR, EA-jet and EA, the West Pacific (WP) pattern is significant to a 95% confidence level. In run 7 ENSO, PNA and NAO have P values slightly above 0.05, however, all of them proved significant to a 95% confidence level in the trend calculations presented in Table 2 (i.e., when the teleconnection patterns were studied one by one). As demonstrated from Table 2, inclusion of ENSO in the regression model increases the summer ozone trend by roughly 0.2 %p/decade. For the remaining teleconnection indices the influence on total ozone trend is very limited (see the last 5 rows of Table 2). [30] Finally, the result from run 7 reveals that POL and SCAND are insignificant explanatory variables with P values above 0.5. Several additional runs (not shown in Table 1) also conforms that POL and SCAND hardly are correlated by total ozone variations in Oslo. [31] As seen from Table 1 there is a large number of explanatory variables in run 7. Normally, the R 2 value will gradually increase as more and more explanatory variables are included in the model, even if the added variables are of no real value. In all our statistical runs this potential problem with overfitting is recognized by an adjusted R 2 value, which takes the degree of freedom into account. If R 2 -adjusted decreases after inclusion of an explanatory variable, the added variable will in general be considered as insignificant and excluded from the analysis. This was the case for SCAND and POL, but the trend estimates in run 7 are the same whether the two teleconnection patterns are included in the model or not. Table 2. Total Ozone Trend Calculated From Regression Analyses With Inclusion of Individual Teleconnection Patterns a Trend Indices Summer Winter None EA/WR 2.5 (D = 0.1) 5.8 (D = 0.3) EA-jet 3.1 (D = 0.7) 6.2 (D = 0.1) EA 2.4 (D = 0.0) 6.4 (D = 0.3) ENSO 2.6 (D = 0.2) 6.1 (D = 0.0) WP 2.5 (D = 0.1) 6.1 (D = 0.0) PNA 2.5 (D = 0.1) 6.0 (D = 0.1) NAO 2.4 (D = 0.0) 6.2 (D = 0.1) SCAND 2.4 (D = 0.0) 6.1 (D = 0.0) POL 2.4 (D = 0.0) 6.0 (D = 0.1) a QBO, F10.7 solar flux, and ADO are used as additional explanatory variables in all the statistical runs. The values in parentheses are trend differences obtained with and without inclusion of teleconnection patterns. 7of11

8 Figure 6. Monthly total ozone trend in Oslo from 1978 to 1998 based on the linear regression analyses in Table 1. The dotted lines represent 95% confidence intervals. (a) Monthly trend calculated from model run 6. (b) Difference between monthly trend obtained from runs 6 and 7. (c) Difference between monthly trend obtained from runs 6 and 8. [32] It is interesting to note that the North Atlantic Oscillation is a prominent pattern which strongly influences the total ozone trend in several European countries. At Arosa and Reykjavik NAO is responsible for changes in winter ozone trends of 0.8 and 3.8 %p/decade, respectively [Appenzeller et al., 2000]. However, the small NAO influence on total ozone trend in Oslo is in good agreement with the analyses from Appenzeller et al. [2000] which conclude that the cross correlation between tropospheric pressure and NAO index in Norway is relatively weak. The NOA index used in run 7 is based on data from CRU (Climatic Research Unit) and is calculated from the Iceland- Azores pressure difference [Jones et al., 1997]. There are several different ways of defining NAO patterns, and an index from CPC-NOAA ( data/teledoc/telecontents.html) has also been tested in our regression model. The latter index shows a much weaker correlation with total ozone in Oslo compared to the CRU data. [33] In model runs 4 to 6 the inclusion of detrended temperature data removes possible effects of climatic changes. Thus by running the regression model without trend-adjusted temperature data, the effect of climatic (i.e., temperature) changes on total ozone trend can be studied. Run 8 (Table 1) is performed with the same explanatory variables as run 5, except that T500 is not detrended. Comparisons of run 8 and 5 shows that this T500 replacement reduces the total ozone winter trend by 0.9 %p/decade, implying that temperature changes explain up to 20% increase in winter ozone trend in Oslo. For the summer season, however, the influence of temperature changes is less pronounced. It is important to note that the winter ozone trend calculated in run 8 is heavily affected by the large temperature increase observed in January (see Figure 4), a temperature trend which is associated with relatively large standard deviation and year-to-year variations. Thus caution should be taken before pretentious conclusions are drawn about winter ozone trend initiated by increased tropospheric temperatures. It is even more important to be careful when nondetrended 100 hpa temperature data are applied in the statistical model, due to the direct coupling between a declining ozone layer and stratospheric temperature decrease. Regression analyses with inclusion of nondetrended T100 data give winter and summer total ozone trends of 4.2 ± 0.9 % per decade and 0.0 ± 0.5 % per decade, respectively, which are very low trend values compared to the other results obtained. [34] When summarizing the regression analyses presented in Table 1, the best model fit is obtained for run 6 (R 2 = 93.9%). We believe that this model, with inclusion of F10.7 solar flux, AOD, QBO and detrended 100 hpa and 500 hpa temperatures, gives the most realistic picture of total ozone trend caused by all anthropogenic sources and climatic/ dynamical changes. The year-round, summer and winter ozone trends are 4.2 ± 0.4% per decade, 2.4 ± 0.5% per decade, and 6.2 ± 0.9% per decade, respectively. As discussed above, circulation changes associated with teleconnection patterns are assumed to have prevented a large negative ozone summer trend from 1978 to Without the circulation anomalies the summer ozone trend in Oslo might have been 1.2 %p/decade higher. Also, the tropospheric temperature changes can probably explain about 0.9 %p/decade of the winter ozone trend observed. Altogether, when climatic and dynamic ozone influences are removed from the D56 time series, roughly 3.5 %p/decade of the summer ozone trend and 5.5 %p/decade of the winter 8of11

9 Table 3. Trend Results Calculated From Regression Models With Different Termination Years a Period Year ± SD, % per decade Summer ± SD, % per decade Winter ± SD, % per decade ± ± ± ± ± ± ± ± ± 0.9 a All model runs include explanation variables for the F10.7 solar flux, AOD, QBO, and detrended 100 hpa and 500 hpa temperatures. ozone trends seem to originate from other ozone depleting sources, such as halogens and greenhouse gases. [35] In addition to estimates of seasonal ozone trends we have studied monthly trend patterns in Oslo. Figure 6 represents monthly total ozone trends for three different model runs. Figure 6a represents the optimal trend result calculated from run 6. As seen from the figure the negative ozone trend in Oslo is significant to a 95% confidence level for all months. The largest negative ozone trend is registered in March ( 8.2% per decade) whereas the smallest ozone decrease is found in June ( 1.2% per decade). Figures 6b and 6c illustrate how monthly ozone trends are affected by the dynamic and climatic anomalies estimated from model runs 7 and 8, respectively. The plots illustrate monthly trend deviations relative to run 6. As seen from the figures, the inclusion of teleconnection patterns and nondetrended temperature data in the regression model has a marked influence on the monthly ozone trend pattern in Oslo Alternative Regression Analyses [36] The regression analyses presented in Table 1 show that the year-round ozone decrease varies from 4.0 to 4.6 %p/decade, depending on the explanatory variables. Another factor that might have significant impact on total ozone trend is the length of the time series analyzed. This aspect is studied from two model runs where the D56 ozone series is shortened and terminates in 1992 and 1993, respectively, i.e., shortly after the Pinatubo eruption. The two scenarios are based on the same regression model and explanatory variables as in run 6 (Table 1). The estimated year-round, summer and winter trends for the two model runs are listed in Table 3, together with the corresponding trend scenario. [37] As seen from Table 3 the summer and winter ozone trends are reduced by as much as 0.9 %p/decade when the termination year is extended from 1992 to The importance of the termination date is especially critical if coinciding with periods where the Pinatubo eruption influences the measurements. Pinatubo injected three times more SO 2 into the stratosphere than the El Chichon eruption [Beig et al., 2002]. Since SO 2 is important for the ozone chemistry [Solomon, 1999; Robock, 2002], the effect of Pinatubo might have been underestimated in our regression model. This can explain why the negative ozone trend is considerably larger when the ozone record terminates in 1993 rather than However, for trend estimates covering the period from 1978 to 1998 a possible underestimation of the Pinatubo effect is not very critical. [38] The analyses presented in Table 1 and Table 3 are performed for SO 2 corrected D56 ozone data. By not taking atmospheric SO 2 interference into account the negative ozone trend in Oslo will on average be 0.5 %p/decade, 0.4 %p/decade, and 0.6 %p/decade larger for the yearround, summer, and winter trends, respectively Influence of Explanatory Variables [39] As demonstrated above, the natural fluctuations in total ozone are influenced by climatic and dynamic variations, volcanic effects and the solar activity cycle. The amplitude or peak-to-peak variations in ozone associated with these parameters can range from only a few DU to more than 50 DU. Table 4 summarizes the effect of the most important explanatory variables on total ozone in Oslo. [40] As we can see from Table 4, the 100 hpa temperature has a very large correlation with total ozone and can explain ozone variations up to 67 DU in run 6 and 72 DU as maximum peak-to-peak value. Compared to T100 the importance of T500 is relatively small and represents ozone variations of only 8 DU in run 6. However, when T100 is excluded from the regression model the influence of T500 is far more pronounced. The AOD index (Aerosol optical depth) is another highly significant explanatory variable Table 4. Influence of Explanatory Variables Explanatory Variable Peak-to-Peak Value, a Z n Ozone Differences in Run 6, b DU Ozone Differences, Maximum Value, c DU T100 D = 12.4 K 67 ± 5 72 ± 5 AOD s ; AOD w D = ± 5; 31 ± 6 35 ± 7; 47 ± 9 F10.7 D = sfu 12 ± 2 17 ± 4 T500 D = 10.7 K 8 ± 4 34 ± 5 QBO s ; QBO w D = 42.8 m/s 6 ± 3; 6 ± 4 12 ± 3; 9 ± 5 EA/WR D =4.8 26±6 EA-jet D =5.4 21±7 EA D =4.0 16±6 WP D =6.2 14±6 PNA D =5.4 13±7 ENSO D = ± 5 NAO D = ± 3 a Peak-to-peak values of the explanatory variables. b Ozone differences (in DU) calculated from the coefficients g n in run 6. c Represents maximum peak-to-peak ozone difference calculated from runs in Table 1. 9of11

10 and is associated with ozone variations of DU in summer and DU in winter. The AOD influence found in this work is slightly higher than the values reported elsewhere [Zerefos et al., 1994; WMO, 1999; Staehelin et al., 1998; Robock, 2002]. [41] The F10.7 solar flux and QBO correlations with total ozone variations are fairly consistent with previous studies. From WMO [1999] it is estimated that F10.7 and QBO are associated with ozone variability of 5 10 DU and 5 15 DU, respectively. Steinbrecht et al. [2001] estimated that F10.7 solar flux and QBO in Hohenpeissenberg represented peak-to-peak ozone changes of DU. However, the German analyses only include February. The last column of Table 4 shows that F10.7 and QBO in Oslo are associated with ozone variations up to 17 DU and 12 DU, respectively. The importance of the explanatory variables in run 6 is considerably smaller due to interaction/ correlation between QBO, F10.7 and temperature. The relatively high influence of F10.7 solar flux found in Oslo can probably be explained by the 22 months time lag used in the regression analyses. Without any time lag the peak-topeak value associated with F10.7 solar flux is reduced to 5 DU in run 6. [42] As seen from Table 4 the teleconnection patterns can also be associated with large variability in total ozone. The East Atlantic/West Russia pattern (EA/WR), the East Atlantic Jet pattern (EA-jet), and The East Atlantic pattern (EA), give maximum peak-to-peak ozone changes of 26 DU, 21 DU and 16 DU, respectively. Also, WP, PNA and ENSO can be related to ozone variability that exceeds 10 DU. It should be noted that the calculated effect of explanatory variables strongly depend on the other variables used in the model. Thus the results presented in Table 4 should only be considered as rough estimates. 4. Summary and Conclusions [43] In this paper we have performed statistical trend analysis of the revised total ozone data from Oslo, Norway, 1978 to The regression analyses are performed for a variety of explanatory variables: QBO, F10.7 solar flux, aerosol optical depth (AOD), temperatures at 100 hpa and 500 hpa levels (T100 and T500), El Niño Southern Oscillation (ENSO), and eight different teleconnection patterns: the North Atlantic Oscillation (NAO), the East Atlantic pattern (EA), the East Atlantic Jet pattern (EA-jet), the East Atlantic/West Russia pattern (EA/WR), the Scandinavian pattern (SCAND), the Polar/Eurasia pattern (POL), the West Pacific pattern (WP), and the Pacific/North American pattern (PNA). Except from SCAND, and POL all the explanatory variables are significant in explaining stratospheric ozone variability in Oslo. [44] The regression analyses presented are performed with 22 months lag in F10.7 solar flux. A series of initial regression analyses demonstrated that the correlation between ozone variations and F10.7 was strongest for 22 months lag in solar flux. The solar influence on total ozone is not fully understood, but our findings are in agreement with previous studies concerning correlation between ozone and sun spot numbers. [45] The optimal agreement between observed and modeled total ozone is achieved when QBO, F10.7, AOD, and detrended 100 hpa and 500 hpa temperatures are used as explanatory variables. For such a statistical model the standard deviation of residuals is smallest (11.2) and R 2 is as high as 93.9%, i.e., the model explains 93.9% of the variations in total ozone. This model is believed to give the optimal presentation of total ozone trend caused by all anthropogenic sources and climatic/dynamical changes. The estimated year-round, summer and winter total ozone trends are 4.2 ± 0.4% per decade, 2.4 ± 0.5% per decade, and 6.2 ± 0.9% per decade, respectively. These results are consistent with work presented by Fioletov et al. [2002] who have studied global total ozone changes from 1979 to 2000, based on satellite data from TOMS and SBUV-SBUV/2. For sites located at approximately 60 N and 10 W the results from Fioletov et al. [2002] show total ozone decreases of 4 5% per decade during winter/spring and 2 3% per decade during summer/fall. The winter trends are somewhat smaller than the results obtained in our study, probably due to differences in termination dates and the fact that the total ozone values were relatively high in 1998 and Stratospheric ozone data presented by WMO [2003] also demonstrate that the seasonal column ozone trends at N and N for the period are consistent with our studies. Both data sets conclude that the total ozone decrease is largest in March and smallest during summer/fall. [46] The D56 trend results described above are estimated with use of detrended temperature data in the regression model. The correlation of temperature changes with total ozone in Oslo is examined from successively statistical analyses performed with and without inclusion of detrended temperature data. When the 500 hpa data without trend adjustments are applied to the model, the winter ozone decrease is reduced by 0.9 %p/decade. Also, the statistical analyses imply that anomalies in teleconnection patterns are responsible for 1.2 %p/decade decrease in the negative ozone summer trend. Altogether, we believe that up to 20% of the observed winter ozone trend and 40% of the summer ozone trend in Oslo can be related to climatic and dynamical changes. Thus the remaining downward trend, i.e., roughly 3.5 %p/decade of the ozone summer decrease and 5.5 %p/decade of the winter ozone decrease, might be explained from chemical reactions involving halogens and greenhouse gases. [47] An important aspect when studying total ozone trend is the start and end date of the time series. 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