Long-term variations of UV-B doses at three stations in

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL., NO. D, PAGES,3-,, AUGUST, Long-term variations of UV-B doses at three stations in northern Europe Jussi Kaurola, Petteri Taalas, and Tapani Koskela Ozone and UV-Radiation Research, Finnish Meteorological Institute, Helsinki Janusz Borkowski Institute of Geophysics, Polish Academy of Sciences, Warsaw Weine Josefsson Swedish Meteorological and Hydrological Institute, Norrkping Abstract. Recent analysis of the total ozone observations indicate a negative trend of about %/decade in the Northern Hemisphere midlatitudes during the last two decades [WMO, 999]. The effect of this decline on surface UV levels is of interest to a variety of applications. In this work the long-term variation of UV radiation at three stations located in northern Europe (Belsk, Norrkping, and Jokioinen) has been studied using data from () ground-based observations, () surface UV doses determined using TOMS satellite measurements, and (3) reconstructed UV doses using observations of global radiation, total ozone, and radiative transfer modeling. For each station the estimates of daily UV doses from various sources have been intercompared, and a trend analysis has been performed to reveal long-term changes in the UV radiation. Data sets, which start in the late 9s or early 9s, show a general positive trend in annual doses of UV radiation. Some of these upward trends are statistically significant. For Belsk the increases are in the range of -% per decade during spring and summer. The largest increases, about %/ decade, has been observed in Norrkping during spring. At Jokioinen there has been a slight upward trend in UV throughout the year. The analysis of reconstructed Belsk data from 9 onward shows that the positive trend since late 9s was preceeded by a negative trend. The reason for such changes is probably not only related to the changes in the total ozone but also to changes in aerosol content and cloudiness. The agreement of the UV series based on different data sources is good. This was studied using a subset of data in which it was required that data from all possible sources were available. The different trend estimates were in very close agreement with each other. However, there were often differences in absolute values, which is probably related to problems in calibration and limitations of the models.. Introduction focuses on the past changes in UV radiation in Northern Europe using data from various sources. The springtime depletion of ozone in Antarctica is a regular The determination of UV radiation at ground level is probphenomenon in the present atmosphere. As a consequence, ably done with most accuracy using in situ measurements with the levels of ultraviolet (UV) radiation have been increasing well-calibrated spectroradiometers. However, these instruments are expensive and laborious to maintain and the spatial over Antarctica and its surroundings [WMO, 999]. The dynamics of the Arctic polar vortex behaves differently, and severe springtime ozone has only been observed occasionally. However, in the 99s there has been reports of severe ozone losses also in the Arctic [WMO, 999]. Recent studies also show that the midlatitude total ozone amounts have been de- clining at the rate of about %/decade in Northern Hemisphere midlatitudes. The important question is if the reduction of ozone has resulted in higher surface UV radiation also in the high latitudes of the Northern Hemisphere. The answer is not self-evident because other effects, such as changes in cloudiness, can compensate for the effect of ozone. This study Copyright by the American Geophysical Union. Paper number JD9. -//JD99.,3 and temporal coverage of the measurements is limited. The procurement of broadband UV meters is much cheaper, but maintenance with high-quality control is also expensive [WMO, 99]. Because of the high costs of maintaining in situ measurement systems it is questionable if a global network with high regional coverage will ever be established. Consequently, there is lack of data over large parts of the Earth, such as oceans and many continental areas, and the knowledge of the long-term changes in UV radiation reaching the ground is limited. However, the UV radiation affects the human beings, and the ecosystems and knowledge of the past variations of UV radiation is important for a variety of impact studies. To overcome the apparent lack of long-term ground-based measurements necessitates the use of other data for the determination of past UV records. In this work we will study past UV changes using in situ measurements, satellite data, and reconstructed data.

2 , KAUROLA ET AL.: LONG-TERM VARIATIONS OF UV-B DOSES N' =. ON' N- N - N - N - N- N- 3N- Figure. Location of the measuring sites. The possibility to estimate UV radiation at ground level using satellite measurements is a new application of remote sensing techniques. The longest continuous data set is available from the Nimbus- TOMS instrument. The TOMS aerosol and UV-B research group at Goddard Space Flight Center, United States, has compiled a data set of daily UV doses for the period of [Herman et al., 99]. The use of satellite data is welcome especially because of its superior spatial coverage. In this work a method for reconstructing past UV data is introduced. The method is based on radiative transfer model- ing and on the use of pyranometer data of global radiation. The purpose of the reconstruction is to have an additional independent data on UV doses and, more importantly, to have an estimate of past variations of UV doses before the instrumental record of UV doses started. In this work the observed record of UV doses and other data sources are analyzed for the following stations (see Figure ): Belsk (.3øN,.øE), Norrkping (.øn,.øe) and Jokioinen (.øn, 3.øE). The analysis is made seasonally based on the daily data.. Analyzed UV Data Sets The analysis is based on daily UV doses weighted by the Commission Internationale de l'eclairage (CIE) action spectrum [McKinlay and Diffey, 9]. The daily UV doses are shown in MED units ( MED = j/m). Belsk UV data have been temperature corrected and homogenized by Borkowski []. In the present work a recent compilation of the data for the period 9-99 is used. This data set is based on the RB measurements before 99 whereafter the Brewer data are used. The old RB meter used so-called "sunburn units," which have been converted to MED units utilizing data that overlapped with the Brewer data. The consistency of the two data sets is discussed by Borkowski []. In Norrkping (.øn,.øe), UV measurements began in 93 with an RB meter. Thereafter, a set of other UV radiometers have been used. These and also a Brewer spectroradiometer have been used for the homogenization and correction of the RB data, which is both temperature and cosine corrected [Landelius and Josefsson, ]. In Jokioinen (.øn, 3.øE) the measurements were started in 99 with a SL biometer. Measurements with a SL were started in March 99 and, finally, with a Brewer spectrophotometer in April 99. Homogenization of the broadband data for Jokioinen is still under development, and therefore these data are not included in the present work. However, a period with well-calibrated double monochromator Brewer data in 99 (T. Koskela personal communication, 99) enabled us to apply the technique of reconstruction to the Jokioinen data and to compare these data with spaceborne UV data (see the following sections for further details). The analysis of the surface measurements is based on daily UV radiation as measured by RB meters. By using a constant factor the doses are converted to doses weighted according to the CIE action spectrum [McKinlay and Diffey, 9] and a corresponding dose of J/m. Because of the difference between the CIE action spectrum and the spectral responsivities of the RB meters this conversion is not perfect. This is because there is a solar zenith angle and total ozone dependency in such a factor [e.g., Bodhaine et al., 99]. Therefore the surface UV data sets are not strictly CIE-weighted UV irradiances. The effect of this on the results is discussed in section... Satellite UV Data The estimation of UV irradiance at Earth's surface using spaceborne remote sensing techniques is new and a fast developing area of research. Methods for deriving surface UV doses rely on the satellite estimation of the most important parameters affecting the atmospheric transfer of UV radiation. These parameters are used as input for a radiative transfer model... Surface UV Measurements The long time series (from November 9 to May 993) of Retrieval of daily UV doses from surface measurements Nimbus- TOMS instrument is of special interest. It makes it requires a careful consideration of the calibration and correcpossible to perform a long-term UV comparison with other tions of individual instruments. In addition, a compilation of data. The TOMS aerosol and UV-B research group at NASA long data sets using data from more than one instrument re- Goddard Space Flight Center has developed an algorithm to for the retrieval of surface UV fields from TOMS measurequires a careful procedure for homogenization of the subdata sets. The task is not trivial, and it deserves critical examination ments [Herman et al., 99, 99; Krotkov et al., 99]. In this work, version of the TOMS UV data has been used. Curof all local information related to the measuring site. The present paper utilizes data that have been described in other rently, the UV algorithm processes data nearly in a global grid papers. In the following, a short introduction is made for data (between øs and øn). In addition to the grid points of the sets of surface measurements with reference given to a more global grid, data have been calculated for some selected locadetailed discussion about the data processing methods. tions. In this work, data for Belsk, Jokioinen, and Norrkping are used. In Europe the longest time series of UV measurements is available from Belsk (.3øN,.øE), where measurements 3. of daily UV doses began in 9 with a Robertson-Berger Reconstruction of Past UV Data (RB) instrument. Since 993, a SL A biometer and a Brewer spectroradiometer have replaced the old RB. The The apparent lack of long-term UV measurements for many stations necessitates the use of other methods for deriving past

3 KAUROLA ET AL.: LONG-TERM VARIATIONS OF UV-B DOSES, UV data. Remote sensing techniques, such as the TOMS UV and air mass characteristics available. The effect of clouds is data sets, make an important contribution to the data sets. However, in order to be able to extend data sets beyond the useful satellite era and also to provide data for comparison and validation of satellite UV data, other data are also needed. The techniques for reconstruction have been introduced, for example, by Bodeker and McKenzie [99], Bordewijk et al. [99], and Madronich [99]. The methods are based either on the modeling of clear-sky UV or on empirical relationships between surface UV and the factors influencing the penetration of UV through the atmosphere. The major shortcoming in the first approach are the clouds. Modeling of radiative transfer of UV in cloudy situations is very difficult due to inadequate cloud information and lack of suitable models. On the other hand, the cloud-free cases are relatively easy to model, at least for situations with low aerosol loading and low surface albedo. A sophisticated empirical approach can give good results for a given location, but the application of such a method for more problematic to take into account. Here we introduce a way to exploit global radiation measurements, which are quite commonly available at many observing stations. In () the ratio of Gob s and G mode can be interpretated as an attenuation factor caused mainly by clouds. This factor is valid for the full solar spectrum. The use of this constant factor over the UV range is, of course, a simplification, but as will be demonstrated, the method seems to give useful results. The role of C in () is to balance for the fact that cloud effects are not the same for UV and global radiation. For successful reconstruction the quality of simulated clearsky irradiances are important. The radiative transfer model should be able to produce the observed irradiances during days with no or very few clouds. Another practical requirement is that the model must cover the full solar spectrum and it should also be fast in terms of computing time because the simulation of long data sets is required for a set of stations. After a survey other locations would probably need recalculation of the param- of available models it was decided to use the SMARTS model eters of the model. In this work a combination of the radiative [Gueymard, 99]. SMARTS model calculates spectral irratransfer modeling and empirical approach is introduced. diances for the wavelength range of - nm. The CIE action spectrum was applied on the calculated spectra, and 3.. Method finally, the daily doses were calculated as an integral of hourly The algorithm for reconstruction of past UV daily doses is a two-step method. First, the clear-sky UV daily dose (Ugmodel) dose rates given by the model. The performance of SMARTS was tested using data from at Earth's surface was calculated with a radiative transfer the stations with low cloudiness. As an example, Figure shows model using the total ozone observation and weather information of SYNOP observations. The total ozone was primarily taken from the ground-based instrument, secondarily from the TOMS total ozone data set, and if none of these was available a value of 3 DU was used. The number of missing ozone values was quite low and the use of a constant value of 3 DU has only a marginal effect on the results. The following information from SYNOP observation was used as input to the radiative transfer model: temperature, humidity, air pressure, and visibility which was used as a proxy for the aerosol loading. With this input data a clear-sky UV was calculated with the model. Thereafter the attenuation caused by clouds was estimated using the ratio of a measured daily sum of global radiation (Gobs) and modeled clear-sky daily sum (Gmodel): G ohs UV...- C G modo UVmodel. () The empirical constant C was calculated using a period of concurrent measurements of UV and global radiation and modeled clear-sky values: UVobs/UVmodel C = Gobs/Gmode. () The values for C were calculated separately for each station. For Belsk and Norrkping, data for years were used, but for Jokioinen only data for one summer was used because the corrected broadband data were not yet available at the time of performing the calculations. The values of C for the stations are quite close to each other: Belsk (.), Jokioinen (.), and Norrkping (.9). The rather low scatter of C among the stations increases the confidence on the usability of the method. The basic idea behind the method is that the modeling of clear-sky UV radiation UVmode for a given location can be done more accurately than the modeling with clouds. The prerequisite for this kind of modeling is, of course, that one has appropriate information on solar zenith angle, ozone, aerosols, the observed and modeled daily UV doses and daily sums of global radiation in Norrkping during the days with low cloudiness. The total cloudiness SYNOP weather observations (,, and UTC) was allowed to be one eighth at maximum. The SMARTS model is able to simulate the global radiation with fairly good accuracy. The mean error, defined as a mean difference to the observed value, is only of the order of %. Also, the root-mean-square (rms) error and the correlation coefficient are showing the accuracy of the model. The UV doses calculated with SMARTS model are under- estimating the observed values in NorrkOping by -%. The high correlation coefficient between the observed and the modeled data, however, implies that the underestimation is rather systematic. The consequence is that the effect of underestimation on the quality of reconstructed data is not serious. The reason for this is that the underestimated modeled clear-sky UV doses are yielding in higher C values (see equation ()), which are compensating the very low SVmode values in (). The underestimation of the UV irradiance by the SMARTS model has also been reported by KOpke et al. [99]. In the present work, however, the underestimation is probably not only related to the general model behavior but also partly related to the choice of surface albedo when making the calculations with the SMARTS model. In the preparatory phase of this work, several tests were made using various settings for surface albedo. No particular albedo could be selected as an outcome of these tests and it was decided to use "zero" albedo in all calculations. The consequence of this choice is that the values of UVmode I in () are lower, but the role of C is, again, to compensate for this matter. 3.. How Well Does the Reconstruction Work? In spite of the apparent simplicity of the method, the reconstructed daily UV doses are in good agreement with the observations. As an example, Figure 3 shows the annual course of daily UV doses for Norrkping in 9. This year can be regarded as a typical year in the data set. As can be seen in the

4 , KAUROLA ET AL.' LONG-TERM VARIATIONS OF UV-B DOSES Olobol redietion [MJ/mt/dey] no. of points= / mean error=. RMS-error=.9 correlation =.99 - '+ UV dose [MED/d y I no. of points= / correlation=.99 / '+ : obs...,,, obs Figure. Observed and modeled daily sums of global radiation and daily UV doses for Norrkping during the days with near-clear skies. top panel of Figure 3, the reconstructed data follows very closely the observed data. The day-to-day variability is about the same in the data sets. The difference between the data sets is shown in the bottom panel of Figure 3. The largest differences are generally less than %. There is a slight tendency of UVreco to be higher than the observed UV in summer; that is, the peaks are slightly higher in the reconstructed data. The opposite is true in spring and autumn. However, the overall performance of the reconstruction is surprisingly good with rather small error and almost randomlike error distribution. In section the method is compared with other data using seasonal averages. of full data sets; that is, the maximum length of each data set is analyzed. This approach takes the full benefit of the lengthy data sets, such as the observed data set for Belsk since 9 and reconstructe data set dating back as early as 9. Secondly, various data sets were compared using a subset of data for each station, in which it was required that all data sets had data available... Annual Averages The annual averages of UV doses are shown in Figure for the longest available period of each data set. The correspond-. Long-Term Changes of UV Radiation UVo. (thick dotted) UV.[co (thin solid The long-term time series of UV data for Belsk, Norrkping, and Jokioinen from various sources is compared in the follow- ing sections: The comparison is based on the seasonal averages of daily UV doses. Because there is some missing data in all >., data sets used in this work, care was taken to avoid differences LU 9 in the results to be resulted from the missing data. Therefore the days with missing data were not filled with time- 3 interpolated data. Instead, the periods were flagged as missing data and were not allowed to affect the analysis. --3 The seasonal averages have been calculated in the following JAN FEB MAR APR MAY JUN JUL AUG $EP OCT NOV DEC 9 way: First, the monthly average was calculated, thereafter the seasonal average was determined. In this way the bias caused U VRECO-- UVoB S by the days with missing data is small. However, if data for. some month are comprised only by data, for example, from the beginning or from the end of a month, then there is a possibility to have bias in the monthly mean. This is especially true.. for spring and autumn months. An inspection of the data sets revealed that this was not the case for any of the data sets analyzed in this paper If less than days of data were available for some individ- - ual month, then the whole month was flagged as missing data If data for some month was missing then the corresponding JAN FEB MAR APR MAY JUN JUL AUG $EP OCT NOV DEC 9 season was also flagged as missing data. Data for winter months (December, January, and February) turned out to be so sparse that results are not analyzed in this paper. Figure 3. Observed and reconstructed daily UV doses for Norrkping in 9. The top panel shows both data sets and Firstly, the analysis focuses on annual and seasonal averages the bottom panel shows the difference.

5 KAUROLA ET AL.' LONG-TERM VARIATIONS OF UV-B DOSES, Table. Trends of CIE-Weighted UV Doses (Percent/Decade) Calculated Using the Longest Period Available for Each Data Set and Station Period Annual Spring Summer Autumn Belsk Obs. May 9 to Dec. 99 (.). (.) 3. Reco. Jan. 9 to Dec TOMS Nov. 9 to May 993. (9.) (.) -.3 NorrkOping Obs. March 93 to Dec. 99 (.)... Reco. March 93 to Dec. 99. (3.) -3.. TOMS Nov. 9 to May (.) 3.. Jokioinen Reco. Nov. 9 to Dec. 99 (.).. (9.) TOMS Nov. 9 to May (.). -.. The trend estimates with higher than 9% confidence level are shown in parentheses. ing linear trend estimates are shown in Table. Because data sets at each station cover different periods it is natural that the trend estimates also vary (see Table ). However, different data sets generally show compatible variations in the annual UV doses although the absolute values may differ. All trends for annual doses are positive with values between >, 3 >, m 3 Belsk' Annual average TOMS Norrkoping' Annual average Jokioinen: Annual average Figure. Annual average of daily UV doses for Belsk, Norrkping, and Jokioinen. and % per decade except the reconstructed data for Belsk, which is the only data set dating back to the 9s. Annual UV doses for all stations are characterized by a large interannual variability. Consequently, the statistical significance of the rather large trend estimates is not high. Most of the estimates are at the edge of the 9% confidence level; some of the estimates exceed that level and some remain below (see Table ). The absolute level of UV doses differ between the data sets, especially in Belsk. The difference between the highest values (TOMS data) and the lowest values (observed data) is about MED/d, which is -% of the observed values. The uncertainty related to the absolute scale is smaller for Norrkping, where the agreement of absolute levels is good. One reason for this might be the cosine correction, which has been applied only to the NorrkOping data [Landelius and Josefsson, ]. The TOMS UV data for Norrkping and Jokioinen had missing data for the winter months, therefore the annual averages of TOMS data for these stations are missing. As a req lt, the only data available for Jokioinen is reconstructed data. Consequently, a truthful comparison with other data is not possible. However, it is interesting to note that the reconstructed data for Jokioinen shows similar year-to-year changes as Norrkping; for example, years 9 and 99 had low values, while years 93, 9, and 99 had high annual UV doses at both stations. The analysis of seasonal data (section.) allows also comparison of TOMS UV and reconstructed data for Jokioinen... Seasonal Averages During spring the UV trends for all stations and all data sets are positive. This may be related to the negative trends in springtime total ozone reported by WMO [999]. The largest increases have occurred since 9s with especially large changes in Norrkping. The long reconstructed data set for Belsk shows, again, the smallest trend. The trend estimates for summer depend on the length of the analyzed data set. For example, in Norrkping all data sets show negative trends for the period of 93-99, while the inclusion of additional years either before (TOMS data) of after (observed data) changes the sign of the trend. The interannual variability of summer values is high, which makes the analysis sensitive to the length of the data set. Generally, one would expect summer UV trends to be smaller than the spring values because the negative trends in summertime total ozone

6 , KAUROLA ET AL.: LONG-TERM VARIATIONS OF UV-B DOSES Table. Trends (Percent/Decade) Calculated Using a Reduced Data Set in Which it Was Required That Data From All Sources Was Available Period Data Sets Annual Spring Summer Autumn Nov. 9 to May 993 March 93 to Dec. 99 Nov. 9 to May 993 Belsk Observed 3. Reconstructed.9 TOMS. Norrkping Observed. Reconstructed. TOMS.. ß Jokioinen Reconstructed. TOMS (9.) (.3) -.3 (3.) (3.) -3.. (.) (.).... The trend estimates with higher than 9% confidence level are shown in parentheses. have been smaller [WMO, 999]. This is the case for Norrk- ping and Jokioinen, while the summer trends for Belsk are of the same magnitude as spring values. The effect of other parameters than total ozone on UV radiation, such as clouds, is probably also important, especially in Belsk..3. Comparison of Data Sets Previous analysis was made using data sets as they were available; that is, the maximum length periods were utilized. However, to compare the different data sets using exactly the same periods, a subset of data sets was prepared. As a result, a reduced data set was compiled with a requirement that all data sets must have a value for a given month in order for that month to be included in the analysis. Typically, the TOMS period was a limiting factor to the length of the analysis. Table shows the results of the comparison. Despite the occasionally large differences in the absolute values, the agreement of trend estimates between the different data sets is good. The dependence of the trend estimates on the length of the analyzed period is especially clear for Belsk, because the reduced data set (Table ) shows much better agreement between the data sets than the full data sets (Table ). For example, the longest data set in Belsk is reconstructe data, which shows a negligible change (.3% per decade) for the whole period (9-99). However, since the late 9s, all data sets show an upward trend, especially in the spring and summer doses. The springtime trends for Norrkping are especially large with high statistical significance and good agreement between the data sets. This suggests that an increase in UV during years has been real. The highest values were observed in 993 when the ozone values were still low after the Pinatubo eruption [WMO, 999]. However, inclusion of years to the analysis (see Table ) suggests that the increase has leveled off during the last few years.. Discussion and Conclusions There is a growing need to estimate the long-term changes in UV radiation. However, the requirement of long, reliable data sets is not often fulfilled and therefore an effort has to be made to take full benefit of all existing data. In this work, data from three sources (in situ instrument, TOMS satellite, reconstructed) have been analyzed for Belsk, Norrkping, and Jokioinen. The long-term changes of UV radiation, based on different data sets, are generally in good agreement. Occasionally, there is a notable difference in absolute values, but the changes for year to year are similar, yielding in quite compat- ible trend estimates. The trends since the 9s for all stations and all seasons studied in this paper were positive with few exceptions. In most lo 9 o lo 9 u % o lo 9 % Obs. Belsk' Spr ng Norrkoping: Spring 9' dokioinen: Spring Figure. Average of daily UV doses in spring (March, April, May) for Belsk, Norrkping, and Jokioinen.

7 KAUROLA ET AL.: LONG-TERM VARIATIONS OF UV-B DOSES,9 cases the increases were of the order of -% per decade. Some of the trend estimates are statistically significant according to the linear trend analysis. The largest increase was found for Norrkping during spring. Madronich [99] calculated clear-sky UV trends using zonal mean TOMS ozone data for the period 99-99, latitudes øn and øn. The annual erythemal UV doses for cloud-free skies had a trend of.% +_.% per decade. Although the region and the analyzed periods differ and the treatment of clouds is different, it can be concluded that the results by Madronich [99] are in broad agreement with the results shown in this paper. The reconstructed data compare quite well to data from other sources. It appears that the combination of radiative transfer modeling and utilization of pyranometer data for global radiation gives a reasonable estimate of real UV doses. For Belsk this method enables one to study changes from 9 onward. The trends over this period were negligible, which suggests that the upward trends in the 9s and early 99s were preceded by some negative trends. The decadal variations in UV are mainly related to variations in ozone and cloudiness. However, the relative role of these variations remain unclear, as based on this work. Most likely, the negative ozone trend reported, for example, in WMO [999] is important. The role of Belsk' Autumn Obs Norrkoping' Autumn ---- TOMS Jokioinen: Autumn Belsk' Summer 3. 3 o>' : Figure. Average of daily UV doses in autumn (September, October, November) for Belsk, Norrkping, and Jokioinen. O u : g' :... Norrkoping' Summer Jokioinen: Summer Figure. Average of daily UV doses in summer (June, July, August) for Belsk, Norrkping, and Jokioinen. aerosols can be significant in areas influenced by industrial activities, such as at Belsk. The results of the trend analysis depend on the length of the analyzed period, which varies because the availability of data varies. However, the agreement of the results between different data sources is good. This was studied using a subset of data in which it was required that data from all possible sources were available. The different trend estimates were in very close agreement to each other. Although the trends calculated from different data sources were in close agreement, there were often differences in absolute values. This is related to problems in calibration and inclusion of, for example, cosine correction in data processing. It is often much easier to measure relative changes than establish the absolute scale. The surface measurements analyzed in this paper are made with the RB meters. The spectral response functions of these meters are not identical with the CIE action spectrum. To derive CIE-weighted doses, the calibration factors of the RB meters should depend on the total ozone and solar zenith angle [e.g., Lantz et al., 999]. However, the Norrkping and Belsk data have been calculated using constant calibration factors. Because of the inverse dependence of the calibration factor on ozone, which is of the order of +% for a %

8 , KAUROLA ET AL.: LONG-TERM VARIATIONS OF UV-B DOSES decrease in total ozone at zenith angles relevant to the accumulation of daily sums [Bodhaine et al., 99], the observed UV trends given in section are likely to underestimate the true trends in the atmosphere. The magnitude of the error cannot be quantified without knowing the actual response function of the instruments used in the current study. Unfor- tunately, such information is not available. The dependence of calibration factors on solar zenith angle is of minor importance for the results presented in this paper because in the seasonal analyses the zenith angles do not change in time. The data in this study have been analyzed without removing the quasi-biennial oscillation (QBO) effect on ozone, which can introduce quasi-periodic oscillations in the UV time series [Zerefos et al., 99]. However, because of the periodic nature of QBO the long-term trends analyzed in this paper are probably not much affected by the QBO. The QBO signal is also weaker at the latitudes where the analyzed stations are located (ø-øn) than at lower latitudes [Zerefos et al., 99]. The removal of the QBO signal would reduce the interannual variability inherent, for example, in Figures -, and the main effect would be to increase the statistical significance levels of the trends in Tables and. Acknowledgments. This work has been a part of an EU-funded project UVRAPPF. We are grateful to our collaborators in the UVRAPPF project for fruitful discussion and support. References Bodeker, G. E., and R. L. McKenzie, An algorithm for inferring surface UV irradiance including cloud effects, J. Appl. Meteorol., 3, -, 99. Bodhaine, B. A., E.G. Dutton, R. L. McKenzie, and P. V. Johnston, Calibrating broadband UV instruments: Ozone and solar zenith angle dependence, J. Atmos. Oceanic Technol.,, 9-9, 99. Bordewijk, J. A., H. Slaper, H. A. J. M. Reinen, and E. Schlamann, Total solar radiation and the influence of clouds and aerosols on the biologically effective UV, Geophys. Res. Lett.,, -, 99. Borkowski, J., Homogenization of the Belsk UV-B series (9-99) and trend analysis, J. Geophys. Res.,, 3-,. Gueymard, C., SMARTS, A simple model of the atmospheric radia- tive transfer of sunshine: Algorithms and performance assessment, FSEC-PF--9, Fla. Solar Energy Cent., Cocoa, 99. Herman, J. R., P. K. Bhartia, J. Ziemke, Z. Ahmad, and D. Larko, UV-B increases (99-99) from decreases in total ozone, Geophys. Res. Lett., 3(), -, 99. Herman, J. R., N. Krotkov, E. Celarier, D. Larko, and G. Labow, Distribution of UV radiation at the Earth's surface from TOMSmeasured UV-backscattered radiances, J. Geophys. Res.,,,9-,, 999. Kpke, P., et al., Comparison of models used for UV index calculations, Photochem. Photobiol.,, -, 99. Krotkov, N. A., P. K. Bhartia, J. R. Herman, V. Fioletov, and J. Kerr, Satellite estimation of spectral surface UV irradiance in the pres- ence of tropospheric aerosols,, Cloud-free case, J. Geophys. Res., 3, 9-93, 99. Landelius, T. and W. Josefsson, Methods for cosine correction of broadband UV data and their effect on the relation between UV irradiance and cloudiness, J. Geophys. Res.,, 9-,. Lantz, K. O., P. Disterhoft, J. J. DeLuisi, E. Early, A. Thompson, D. Bigelow, and J. Slusser, Methodology for deriving clear-sky erythemal calibration factors for UV broadband radiometers of the U.S. Central UV Calibration Facility, J. Atmos. Oceanic Technol.,, 3-, 999. Madronich, S., Implications of recent total atmospheric ozone mea- surements for biologically active ultraviolet radiation reaching the Earth's surface, Geophys. Res. Lett., 9, 3-, 99. McKinlay, A. F., and B. L. Diffey, A reference action spectra. for ultraviolet induced erythema in human skin, in Human Exposure to Ultraviolet Radiation: Risks and Regulations, edited by W. R. Passchier and B. M. F. Bosnajakovich, pp. 3-, Elsevier, New York, 9. World Meteorological Organization, (WMO), Global Atmosphere Watch, WMO Rep., WMO TD-9, Geneva. WMO, Scientific Assessment of Ozone Depletion: 99, WMO Rep., Global Ozone Res. and Monit. Proj., Geneva, 999. Zerefos, C. S., K. Tourpali, and A. F. Bais, Further studies on possible volcanic signal to the ozone layer, J. Geophys. Res., 99,,-,, 99. Zerefos, C., C. Meleti, D. Balis, K. Tourpali, and A. F. Bais, Quasi- biennial and longer-term changes in clear-sky UV-B solar irradiance, Geophys. Res. Lett.,, 3-3, 99. J. Kaurola, P. Taalas, and T. Koskela Ozone and UV-Radiation Research, Finnish Meteorological Institute, POB 3, Helsinki, Finland. (jussi.kaurola@fmi.fi; petteri.taalas@fmi.fi; tapani.koskela@ fmi.fi) W. Josefsson, SMHI, S- Norrkping, Sweden. (weine. josefsson@smhi.se) J. Borkowski, Institute of Geophysics, Polish Academy of Sciences, Ks. Janusza, PL- Warsaw, Poland. (borek@igf. edu.pl) (Received September 9, 999; revised April, ; accepted April,.)