Long term solar erythemal UV irradiance data reconstruction in Spain using a semiempirical method

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116,, doi: /2011jd015836, 2011 Long term solar erythemal UV irradiance data reconstruction in Spain using a semiempirical method Julia Bilbao, 1 Roberto Román, 1 Argimiro de Miguel, 1 and David Mateos 1 Received 22 February 2011; revised 16 September 2011; accepted 19 September 2011; published 29 November [1] This paper proposes a semiempirical method to reconstruct ultraviolet erythemal (UVER) irradiance in the past from total shortwave radiation (SW) and total ozone column (TOC) measurements and has been used to obtain a long term reconstructed UVER series in central Spain. The method is based on radiative transfer modeling combined with empirical relationships, giving an equation that relates UVER and SW irradiance measurements, solar zenith angle, as well as UVER and SW irradiance values calculated under cloudless conditions. TOC measurements are needed as input for the cloudless modeling. Hourly UVER radiation values have been reconstructed and compared with ground based measurements for seven Spanish locations. The reconstructed hourly UVER irradiance values are in good agreement with the measurements, showing a determination coefficient between 0.95 and 0.99, and the lowest root mean square errors (rmse) in summer taking values from 5% to 9% in the seven stations. Reconstructed daily UVER doses have been compared for eight stations, showing a better agreement than in the hourly case with rmse values from 3% to 8% in summer and from 4% to 9% when all seasons are taken into account. A reconstructed 10 min UVER irradiance data set from 1991 to 2010 has been calculated using the proposed method for the city of Valladolid. Statistically significant UVER trends appear in summer and autumn when UVER levels increased 3.5% and 4.1% per decade, respectively. Brightening was found for SW measurements in the same period, with a statistically significant trend of 4.4% and 5.8% per decade in summer and autumn. Citation: Bilbao, J., R. Román, A. de Miguel, and D. Mateos (2011), Long term solar erythemal UV irradiance data reconstruction in Spain using a semiempirical method, J. Geophys. Res., 116,, doi: /2011jd Introduction [2] UV solar radiation on Earth is a small fraction of total radiant solar energy and has been studied in recent decades since it is involved in photochemical reactions and biological processes. In particular, ultraviolet (UVB) has a major biological importance, as photons in this spectral band may damage molecules and proteins of living organisms, entailing detrimental effects for humans, animals, and ecosystems [Diffey, 1991; World Health Organization, 1995]. However, UVB is also essential for the synthesis of vitamin D, which is beneficial in the prevention of certain diseases [Webb, 2006]. [3] In order to assess the effects of UV and enhance current knowledge of UV photobiological damage, biologically active erythemal solar radiation levels, ultraviolet erythemal (UVER) [McKinlay and Diffey, 1987], in the past, present, and future must be researched. It is essential to know the changes and the evolution of UV in the past since many biological and health related effects depend on long term 1 Atmosphere and Energy Laboratory, Applied Physics Department, Valladolid University, Valladolid, Spain. Copyright 2011 by the American Geophysical Union /11/2011JD UV dose accumulation. Reliable measurements have usually been available since the late 1980s [Lindfors et al., 2007]. The longest measured UVER data set in Spain commenced in 1995 for Madrid and was measured by the Spanish Meteorological Agency (AEMET). However, SW measurements had been recorded earlier, for example, since 1991 in Valladolid by AEMET. UVER data reconstruction methods are important because of the scarce number of available long term UVER measurements. [4] Several authors have proposed statistical relationships between measurements of several geophysical parameters such as total ozone column (TOC), total shortwave radiation (SW), cloud cover or solar zenith angle (SZA) [e.g., Fioletov et al., 2001; Díaz et al., 2003; Esteve et al., 2009; Mateos et al., 2010; Bilbao et al., 2011]; however, these relationships are normally site dependent. [5] Another methodology uses radiative transfer and empirical models to estimate UV values under cloudless conditions, followed by correction of cloud effects based on either SW [Bodeker and McKenzie, 1996; Kaurola et al., 2000; den Outer et al., 2005] or sunshine duration measurements [Lindfors et al., 2003; Lindfors and Vuilleumier, 2005]. For UVER reconstruction, Reuder and Koepke [2005] used radiative transfer model calculations and differ- 1of15

2 ent algorithms to derive the required input parameters, i.e., total ozone and surface albedo. More recently, Lindfors et al. [2009] developed a method for spectral UV reconstruction using radiative transfer simulations. In general, existing reconstruction methods require long data series and simultaneous measurements that are available at few radiometric stations. [6] Chubarova [2008] simulated long term UV measurements using a reconstruction model in Moscow, Russia, and showed that clouds had the most significant role in the UVER trend. Rieder et al. [2010] compared reconstructed and observed erythemal data and analyzed the atmospheric conditions that provided the highest daily UVER dose in three stations in central Europe in the period using the reconstructed database. den Outer et al. [2010] tested five different reconstruction models using data measured at eight European locations. Two of these models [den Outer et al., 2005; Kazantzidis et al., 2006] were based on radiative transfer modeling combined with empirical relationships, as is the proposed model in this paper; long term data reconstruction was carried out using data from the 1960s until the 2000s. One of these models used the Best Estimate algorithm, based on an idea exploited in UV intercomparison campaigns [Slaper and Koskela, 1997]. [7] Most existing works on UV series reconstruction have been based on data from north and central Europe and from North American, Canadian, and Australian stations. Over the last decade, reconstruction studies for several regions in Europe, especially in Nordic and central European countries have been conducted [e.g., Lindfors et al., 2007; Rieder et al., 2008]. It is worth mentioning that some reconstruction models are based on data from locations often at higher latitudes than those of the Iberian Peninsula and with a great influence of surface albedo that is due to snow cover. Moreover, few studies combine the effects of attenuation factors (such as ozone, aerosols, and clouds) and radiative transfer model evaluations to obtain a model that would be global and assessed at middle latitudes and therefore on the Iberian Peninsula. [8] After reviewing existing studies, a different method was proposed based on data from Valladolid (Spain). One aim of the current work was to obtain a semiempirical model of UVER from SW measurements through cloud modification factors. The advantage of this method is that all atmospheric information (particularly concerning clouds) in SW measures is transported to the UVER range. When the model was developed, the objective was to validate the proposed method using UVER records from other sites to guarantee the quality of the model. Finally, the main aim was to analyze UVER temporal trends of a long term UVER data series that was reconstructed using the proposed model. [9] The interest of this paper is that the proposed method for reconstruction is semiempirical and fast and the equation is unique, in addition to which it has been tested with large data sets from different stations. The model can be used to reconstruct 10 min UVER irradiance data, which is a high temporal resolution. A reconstruction model is interesting in Spain because of the high UV levels recorded and, in consequence, for evaluating UVER trends in the past and completing long term UVER climatological series. Moreover, no similar work has previously been carried out in central Spain, and the calculated UVER series in this work is the first and longest reconstructed UVER data set available in Spain, covering a total of 20 years (in Valladolid from 1991 to 2010). [10] The site description, sensors, collection, and quality control of data are detailed in section 2. The development of the proposed model is described in detail in section 3. Results and discussion are provided in section 4, where the proposed model is tested, and applied to reconstruct and analyze a long term UVER data set. Finally, the conclusions can be found in section Measurements, Data, and Procedure 2.1. Data [11] The solar radiometric station (Valladolid SRS; N, 4 56 W, and 848 m above sea level) at the University of Valladolid, Spain, is located in a rural area, some 35 km NW of the city of Valladolid, in the autonomous region of Castilla y León (a region covering one fifth of the whole country). The region s climate is strongly influenced by Atlantic air masses, and the weather at the site is usually warm in summer (June September) with a maximum temperature of 38 C and a minimum of 10 C, while in winter (December March), it is influenced by Atlantic frontal systems, with temperatures ranging from a maximum of 10 C to a minimum of 5 C. Population density (around 26 people per km 2 in the region of Castilla y León) and the industrial emissions are not very high and therefore aerosol load is low [Bilbao et al., 2003]. [12] UVB irradiance was recorded by a pyranometer (YES UVB 1), and SW ( nm) was also recorded by a pyranometer (Kipp & Zonen CM 6B), both over a horizontal surface. Maintenance was carried out at the station every week and included instrument cleaning, bubble leveling of the instruments, and monitoring of desiccant state [Bilbao et al., 2008]. [13] According to the manufacturer s specifications, UVB 1 has a spectral response between 280 and 315 nm, very similar to the erythemal (sunburn) action spectra. Its cosine response is better than ±5% for solar zenith angles in the range 0 60, and sensitivity is 2.04 W m 2 V 1. UVB 1 is calibrated by periodically measuring its spectral response and comparing ground measurements each year with other calibrated UVB 1 taken as a reference. The reference sensor was last calibrated at the World Radiometric Center (Davos, Switzerland) in 2009; this calibration consisted of sensor spectral response evaluation being measured indoors and a comparison being carried out with a Brewer MKIII spectroradiometer outdoors [Vilaplana et al., 2006]. After the calibration process, a matrix was obtained, for the UVB 1 used at Valladolid SRS, with elements C ij, which correspond to different solar zenith angles and total ozone column values. In order to convert the output voltage signal of the sensor into units of erythemally effective W m 2 [Webb et al., 2006], the voltage signal was multiplied by the matrix calibration factor taking into account SZA and TOC corresponding to the time and day, respectively. After this process, the corresponding UVER in effective W m 2 is obtained. The experimental uncertainty of the sensor following the results of Hülsen and Gröbner [2007] lies in the range 4.6% 7%. 2of15

3 Table 1. Monthly Values of the Aerosol Optical Depth, AOD 500, and Water Vapor, TWVC Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AOD TWVC (cm) [14] SW data were recorded by the CM 6B pyranometer that provides measurements in a spectral range from 305 to 2800 nm. It is calibrated each year by another CM 6B used solely for this purpose [Bilbao and de Miguel, 2010]. The latest calibration was carried out in 2009 at the Institute of Renewable Energy, (Ciemat), Madrid, Spain, through an outdoor intercomparison with a reference sensor (Kipp & Zonen CM22, sn ) traceable by the World Radiometric Reference through the World Radiometric Center. The calibration constant has uncertainties below 2%, and the measurement error is below 5% according to sensor specifications. [15] The solar sensors were connected to a Campbell CR23X data logger, which was programmed to take measurements every 10 s and to compute average values every 10 min. Hourly irradiance was then evaluated as the average of the 10 min values for each hour, from which daily irradiation was then obtained. [16] Daily TOC was obtained by satellite based remote sensing. TOC data from TOMS/OMI (downloaded from collected in the Earth Probe and Aura satellites, and GOME and GOME 2 data (supplied by the staff of the Remote Sensing Technology Institute, IMF, of the German Aerospace Centre, DLR) were used. Daily TOC data from July 2002 to December 2009 were used in this paper. TOMS data from 2002 to 2004 were TOMS V8 corrected when the TOMS instrument was in the Earth Probe satellite during this period. This corrected version showed a deviation below 1% for latitudes similar to Valladolid when compared with ground measurements [Antón et al., 2010]. [17] Values of aerosol optical depth at 500 nm (AOD 500 ) and total water vapor column (TWVC) are used in the work for cloudless skies. These values were obtained from the climatology tables published in the AERONET network ( corresponding to the nearest station in Palencia, Spain. Table 1 shows the monthly average aerosol and water vapor values used in the evaluations. [18] In addition, a large volume of data was collected in the context of the work to study the performance of the proposed model in Spain. Seven different AEMET radiometric stations were chosen apart from the Valladolid SRS station itself. The main criterion for selecting stations was data completeness concerning the availability of aerosol optical depth and satellite based ozone values together with the characteristics and length of the data series. The stations provide measured values of hourly SW and UVER solar radiation. All sensors are calibrated every second year by a comparison with a reference calibrated at the World Radiometric Center, and the spectral response is also measured for the UVB 1 pyranometers. Maintenance tasks are carried out every week following the guidelines described by Webb et al. [2006]. A summary of the selected sites is presented in Table 2. Figure 1 shows the situation of the selected stations and the corresponding climatic zones in Spain: Atlantic (I), North Mediterranean (II), Mediterranean continental (III), and Mediterranean maritime (IV). [19] One of the radiometric stations described above was used for UVER reconstruction data. This station is in the city of Valladolid and is referred to as Valladolid AEMET in this paper. SW UVER data pairs are shown in Table 2 ( ). Hourly SW data from 1991 to 2010 were also available and were used to reconstruct hourly UVER data from 1991 at this station. Moreover, ozone data from 1991 were necessary to calculate radiation under cloudless conditions. Ozone data were provided by TOMS/ OMI (Earth Probe and Aura satellites), GOME, and GOME 2, although the ozone series was also completed using ozone data from TOMS in Nimbus 7 and Meteor 3 satellites. Moreover, some gaps (e.g., a lot of satellite data are not available from 1992 to 1996) were completed using ozone ground measurements from the AEMET station in Madrid (Ciudad Universitaria), around 200 km from Valladolid. These data were measured by a Brewer MKII spectroradiometer. In fact, ozone satellite data in Valladolid were compared with ozone ground measurements in Madrid, the results showing that 85% of data present a deviation below 5% for 5765 analyzed data. Ozone data in Valladolid were therefore assumed to be the same as in Madrid, the total number of ozone data available in the Valladolid AEMET station from 1991 to 2010 amounting to Similar quantities of ozone at the two sites may be explained because the highest ozone quantity is in the stratosphere, Table 2. Geographical Locations and Database for This Study Location Period Latitude ( N) Longitude ( W) Altitude (m asl) Temporal Resolution Sensors (SW; UVER) SW UVER Data A Coruña Hourly CMP21; YES UVB 1 3, Granada (Base aérea) Hourly CM 11; YES UVB 1 4, León (Virgen del Camino) Hourly CM 11; YES UVB 1 1, Madrid (Ciudad Universitaria) Hourly CM 21; YES UVB 1 4, Murcia Hourly CM 21; YES UVB 1 4, Valladolid (AEMET) Hourly CM 21; YES UVB 1 35,709 4,011 Valladolid (SRS) min CM 6B; YES UVB 1 105,500 2,871 Zaragoza (Valenzuela) Hourly CM 21; YES UVB 1 3, TOC Data 3of15

4 Figure 1. Distribution of selected stations in Spain and climatic zones. this layer suffering few variations in its properties with distance over the Iberian Peninsula Data Quality Control [20] Because of the different cosine responses of the instruments, only data with an SZA below 80 were considered for all stations. Hourly values were calculated integrating 10 min data only for hours without data gaps. Daily values were obtained as the temporal integration of hourly values when all hourly values were available for each day. [21] The rest of this section explains in detail the data quality control for the Valladolid SRS station. The UVER irradiance series therefore comprised 115, min diurnal data. A total of 1430 values was rejected because ozone data were not available. Finally, 114, min data were selected and used in this paper. A total of 1488 UVER daily values was obtained from 13,490 UVER hourly data. Several data values were missing in 2006, 2007, and 2008 because of calibration activities and technical problems. [22] The SW data set consisted of 167, min values under SZAs below 80. SW was checked, taking into account the detection limits of the sensor and extraterrestrial solar irradiance values. The quality control applied is explained in detail by Bilbao et al. [2011]. From 164, min SW data, a total of 29,464 hourly and 2732 daily values were obtained, respectively. [23] The time period analyzed in this study is from July 2002 to December A total of 105, min data UVER SW pairs were obtained (Table 2), with 39,414 pairs from 2007 to 2009 being used to evaluate model performance Procedure [24] The work may be described by four steps. [25] 1. Simulating UVER and SW irradiances under cloudless conditions with a radiative transfer model and evaluating their errors compared with those of cloudless experimental data. The accuracy of the cloudless simulated data was assessed comparing the measured and simulated data by means of three widely used statistics, root mean square error (rmse), mean bias error (mbe), and mean absolute bias error (mabe) defined as follows: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P u N t ðx i esti X i meas Þ 2 i¼1 rmse ¼ N vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1þ P N rmse ð% Þ ¼ 100% u t ðx i esti X i meas Þ 2 i¼1 N mbe ¼ P N i¼1 X M ðx iesti X imeas Þ N mbe ð% Þ ¼ 100% X M mabe ¼ P N i¼1 P N i¼1 jx i esti X i meas j N mabe ð% Þ ¼ 100% X M P N i¼1 ðx iesti X imeas Þ N jx i esti X i meas j where X iesti is the reconstructed value, X imeas is the measured value, X M is the average of the UVER measured value, and N is the number of data points in the study. Another well known statistical index used was the determination coefficient r 2.In addition, the data were analyzed for the four seasons: spring (March, April, and May), summer (June, July, and August), autumn (September, October, and November) and winter (December, January, and February). [26] 2. Finding a relationship between measured and cloudless simulations of UVER and SW irradiances and using it to infer all sky UVER. This step is performed using Valladolid SRS data from 2002 to N ð2þ ð3þ 4of15

5 Table 3. The rmse, mbe, mabe, Number of Data, and Determination Coefficient of the Comparison Between Cloudless Values (Measured and Estimated), for Valladolid SRS Station Irradiance Season rmse (W m 2 ) mbe (W m 2 ) mabe (W m 2 ) N r 2 UVER Spring (5.5%) ( 0.8%) (3.7%) 1, Summer (5.3%) (0.5%) (3.9%) 3, Autumn (5.5%) (0.5%) (3.8%) 1, Winter (7.5%) ( 0.4%) (5.6%) All (5.5%) (0.2%) (3.9%) 7, SW Spring 21.9 (3.5%) 1.8 (0.3%) 16.3 (2.6%) 2, Summer 16.6 (2.6%) 1.3 (0.2%) 13.7 (2.1%) 5, Autumn 23.1 (4.5%) 4.4 ( 0.8%) 17.6 (3.4%) 2, Winter 17.7 (3.9%) 4.0 ( 0.9%) 15.2 (3.4%) All 19.8 (3.3%) 0.2 (0.0%) 15.3 (2.5%) 11, [27] 3. Testing the model using the Valladolid SRS data ( ) and measurements from other Spanish localities. Cloudless simulations for the other locations were performed applying the same explained method as in the Valladolid SRS station, using corresponding TOMS/OMI TOC data and monthly values of AOD 500 and TWVC from the nearest AERONET station. [28] 4. Using the proposed model to reconstruct UVER data from 1991 to 2010 at the Valladolid AEMET station. This new series was analyzed and temporal trends were established. Linear fits (UVER as a function of year) were calculated and trends were considered as the ratio between the fit slope and the UVER average used in the fit. The last values were multiplied by 100 to obtain the unit: % yr 1. The same method was used to obtain the SW and TOC trends. The statistically significant trends were selected calculating the 95% confidence interval and the standard error for the correlation coefficient r. In this study, a trend was considered to be statistically significant when the standard error of r (95% confidence) was below the absolute value of r. A similar method was used by Josefsson [2006]. 3. Modeling UVER Radiation 3.1. Cloudless Sky Conditions [29] In order to estimate cloudless sky irradiance, two different methods can be used: empirical evaluation and radiative transfer model code. In this case, radiative transfer was used to evaluate 10 min UVER clear and SW clear data through integration of spectral simulated values. The great advantage of radiative transfer models is their universal behavior. [30] There are several radiative transfer models to obtain estimations about solar radiation under cloudless sky conditions. Two techniques were tested to this end. The first uses the new version of the DISORT method solver of the radiative transfer equation [Stamnes et al., 1988] in the LibRadTran 1.4 library [Mayer and Kylling, 2005]. The second calculates spectral radiation from spectral transmittance functions of the main extinction processes under cloudless conditions: SMARTS [Gueymard, 2005]. These two models were compared and the agreement was good with a difference of less than ±6% for climatological conditions in Valladolid SRS. The SMARTS model, whose algorithms are faster, was thus chosen to carry out the estimation of 10 min horizontal UVER and SW, both under cloudless skies. [31] The SMARTS code is available at gov/rredc/smarts/. In this work, total ozone, surface albedo, and aerosol optical depth were used as additional variable input parameters for the model simulations. The extraterrestrial spectra necessary for evaluation were provided by Gueymard [2004]. The derivation of all required input parameters is described below. [32] Atmospheric composition profiles were taken from the U.S Standard Atmosphere [Anderson et al., 1986]. The daily TOC was obtained by satellite based remote sensing. Aerosol effects were simulated by the S&F_RURAL aerosol model [Shettle and Fenn, 1979] embedded in SMARTS, and the model input was the monthly average of AOD 500 provided by AERONET. Water vapor data were the monthly values provided by AERONET at the Palencia station. The surface albedo in the simulations was set to be Lambertian and wavelength independent. Natural areas usually have a surface albedo below 0.1 in the UV spectral range. In the case of snow cover, however, ground albedo can reach values above 0.8. The values chosen for UVER and SW irradiance simulations were 0.03 and 0.20, respectively [Koepke et al., 2002]. Spectral resolutions were 0.5 nm for nm, 1 nm for nm, and 10 nm for nm. [33] The agreement between the cloudless modeled and measured irradiances was tested using the measurements under cloudless conditions from Valladolid SRS ( ). Cloudless measurements were chosen by visual selection of whole cloudless days. The results are shown in Table 3 and indicate a good agreement between measurements and calculations. The determination coefficient is about 0.99 in both cases, the rmse is higher in the UVER case (5.5%) than in SW (3.3%), and both modeled values show a null bias. There is no significant dependence on season, although the modeled values underestimate SW radiation in autumn and winter, showing the mbe close to 1%. In principle, such tuning of parameters can lead to faulty cloudless simulations if measurements have bias. However, there is no reason to question the accuracy of the measurements because of the instrument calibration practices and quality control measures described in section 2. Moreover, the chosen input parameters are in the range of expected values All Sky Conditions [34] The first step involves developing the UVER reconstruction model for all sky conditions, called UVER RECO, by means of relationships between cloud effects on UVER and SW irradiances. The cloud modification factors for UVER, CMF UVER, and for total shortwave irradiance, 5of15

6 Figure 2. Relationship between CMF UVER and CMF SW for four different fixed solar zenith angles in Valladolid SRS, Spain, during the period The solid line is the power fit given by equation (6). CMF SW, are defined as the ratio between measured and calculated irradiances under cloudless sky conditions and are given by [Calbó et al., 2005] CMF UVER ¼ UVER meas UVER clear ; CMF SW ¼ SW meas SW clear ; where meas indicates measured value and clear is calculated data under cloudless skies using the radiative transfer model mentioned above. [35] UVER solar radiation at ground level is influenced by solar geometrical factors and atmospheric constituents such as TOC. Geometrical variations can be removed by comparing irradiance at fixed SZA and, in consequence, solar radiation changes are mainly controlled by atmospheric constituents. For this reason, the relationships between 10 min CMF UVER and CMF SW were selected at four different fixed SZAs corresponding to a narrow range (±0.1 ) of 20, 40, 60, and 70, and were drawn in Figure 2 (data from July 2002 to December 2006). The graphs show potential behavior, with a tendency toward a linear shape when the SZA decreases to low values. When the SZA increases, the fits evidence greater curvature. There is more dispersion among values when CMF is close to 1. Moreover, dispersion is highest when the SZA is 70, in addition to which unusual values appear with a high error showing a ð4þ ð5þ CMF UVER above 1 with a CMF SW below 1, and vice versa. This is due to the error in the cloudless estimations, and, in the case of SZA = 70, higher dispersion is also provided by the error in the measurements (cosine error). Figure 2 shows that SW attenuation by clouds is higher than UVER one for all SZA values, and the difference between UVER and SW attenuation by clouds increases with the SZA. Similar results have also been obtained by different authors [e.g., Blumthaler et al., 1994]. Hence, CMF UVER can be given by the following proposed expression: ðcmf UVER Þ SZA¼CTE ¼ AðCMF SW Þ B ; ð6þ where A and B are parameters that explain curvature. A similar expression was obtained by Bodeker and McKenzie [1996]. Equation (6) must satisfy two conditions: CMF UVER must be zero when CMF SW is zero, and CMF UVER must be one when CMF SW is one. This means that the A parameter must be removed from equation (6), giving ðcmf UVER Þ SZA¼CTE ¼ ðcmf SW Þ B : ð7þ The last equation has a more realistic meaning than the other models. The B parameter evidenced a significant dependence on the SZA cosine. As a result, intervals of the SZA cosine with a width of 0.01 were taken from 0.18 to 0.95, giving a total of 78 intervals. With data from CMF UVER and CMF SW included in each SZA interval, 78 different fits were performed. Figure 3 shows the 78 values for parameter B as a function of cosine SZA. As expected, B falls when SZA 6of15

7 equation (11) using previously calculated CMF values from the 10 min erythemal and total shortwave irradiance data between 2002 and The obtained fit parameter results are c = ± and d = ± [37] Once the fit parameters were calculated and used in equation (10), the final expression for the proposed reconstruction model becomes UVER reco ¼ SW ½0:382 þ 0:659 cos ðszaþš meas UVER clear : ð12þ SW clear Figure 3. B parameter as a function of cos (SZA). The corresponding regression fit and number of data are also given for Valladolid SRS, Spain. increases. Figure 3 also shows a fit function for the B parameter whose analytical form is given by Equation (7) can be written as BðSZAÞ ¼ c þ d cosðszaþ: ð8þ ½ c þ d cos ð SZA ÞŠ CMF UVER ¼ CMFSW : ð9þ By taking the CMF definitions into account, equation (9) can also be written as UVER reco ¼ SW ½c þ d cos ðszaþš meas UVER clear ð10þ SW clear or logðcmf reco Þ ¼ c logðcmf SW Þþd cosðszaþlogðcmf SW Þ: ð11þ [36] By means of a linear regression and the least squares method, the model coefficients c and d were evaluated from [38] The proposed model given by equation (12) indicates that cloud transmittance is similar in both ranges (UVER and SW) for lower SZA values, as equation (8) is close to 1 when SZA is close to zero. 4. Results and Discussion 4.1. Model Performance Test [39] The UVER RECO model was developed using Valladolid SRS measurements from 2002 to 2006, and its performance was tested with measurements from the Valladolid SRS station for the period Figure 4 shows the 10 min UVER irradiance values in Valladolid SRS for four days by way of a comparison among measured and estimated values and under cloudless skies. The plotted days (random choice) were selected from different seasons. It can be seen that the proposed model offers high resolution and that measured and estimated UVER irradiance values are similar for all 10 min data. [40] The ratio between estimated and measured UVER 10 min irradiance as a function of the SZA is shown in Figure 5 for Valladolid SRS. The solid horizontal lines include values with a difference below 10% with respect to 1, while the dashed horizontal line shows ideal agreement, the open circles and error bars being the mean and standard deviation, respectively. Results indicate that 82% of the ratio is between 0.9 and 1.1 for SZA below 70. This percentage Figure 4. Comparison of 10 min measured (gray points), estimated erythemal (black straight line) and clear sky (gray line) UV solar irradiance evolution for four days and different seasons, in Valladolid SRS, Spain. 7of15

8 Figure 5. Ratio of 10 min measured to estimated erythemal UV irradiance as a function of SZA in Valladolid SRS, Spain, during the period Horizontal lines mean ratio values of 0.9 and 1.1 (solid), and 1 (dashed). Vertical dashed lines indicate SZA of 60 and 70. Open circles are the ratio averages, and error bars are their standard deviation. reaches 89% for SZA below or equal to 60. Table 4 shows the mean and standard deviation values of the ratio represented in Figure 5 for different SZA intervals. The standard deviation of the ratio increases with SZA, and the low values of the ratio indicate that the proposed model underestimates the measurements for high zenith angles. The result obtained for SZAs between 35 and 45 is better than that obtained by Lindfors et al. [2007] in Bergen (Norway) for hourly values (mean ratio of 0.99 and standard deviation of 0.08), although for higher SZA intervals the ratio shows poorer mean and standard deviation values. [41] The comparison between 10 min measured and simulated values can be seen in Figure 6a. Statistical indices are given in Table 5 showing rmse values below 8.3% (for all sky conditions) except in winter, when the model underestimates measurements, which are connected to the prevailing large solar zenith angles in this season. The model underestimates more in winter than in the other seasons for SZAs between 45 and 65 (see Table 4), which could be explained by adding the snow effect. Some winter days with snow cover (27 days from 2007 to 2009 measured by AEMET staff in the nearest station to Valladolid SRS) might influence the results because of the high albedo, which is not taken into account in the cloudless modeling. For all seasons, the model shows an rmse of about 7% and a slight underestimation with a bias of 1.1%. The enhancement effect, which is increased solar radiation levels at the surface that are due to the presence of clouds [Calbó et al., 2005], is also shown for selected values with two conditions: CMF SW above 1.15 (greater than 15% compared with cloudless skies), and SZAs below 60. These conditions guarantee an enhancement effect [Sabburg and Parisi, 2006]. The model predicts enhancement with an rmse value below 10%. However, mbe and Figure 6b indicate that modeled data overestimate the enhancement effect. Despite this, measured and reconstructed 10 min UVER irradiance data are in good agreement and show high correlation. [42] A comparison of statistical estimators, rmse, mbe, mabe, and determination coefficients for hourly UVER measured and estimated irradiances in AEMET stations is given in Table 6. In general, and for all seasons, the rmse is below 11%, the maximum value (10.2%) appearing in A Coruña, on the North Atlantic coast (climatic zone I). The best rmse values are obtained in Granada, Zaragoza, and León, with the worst mbe values appearing in Murcia and A Coruña. The worst results are in climatic zones I and IV and the best in zone III. This might be because the model was developed using data from Valladolid SRS (zone III). The role of aerosols could be another factor influencing the results obtained in A Coruña and Murcia since the monthly AOD 500 values used in the cloudless simulations for these stations were taken by the nearest AERONET stations, although these are in climatic zone III. Winter shows the worst agreement at all stations, which might, as indicated above for the Valladolid SRS station, be mainly due to the high solar zenith angles in this season. The determination coefficient evidences high values, ranging from 0.97 to [43] Graphical comparison of hourly UVER estimated and measured data at the seven selected AEMET stations is given in Figures 6c 6i. The dashed line represents the ideal match between estimated and measured values. The influence of the geographical location (latitude) on UVER irradiance levels shows that maximum UVER values in A Coruña are lower, 0.22 W m 2, than those recorded in Granada or Murcia, 0.28 W m 2. Comparing the seven scatterplots and considering the results in Table 6, it can be deduced that all scatterplots show similar behavior and that the model performs well for all stations. [44] A comparison between daily measured and simulated UVER data for the eight Spanish stations is given in Table 7. The determination coefficient shows very high values ranging between 0.90 and 0.99, the rmse values ranging from 3% in Valladolid SRS (spring) to 11% in A Coruña (winter). Summer and spring seasons show the best agreement between measurements and modeled data. Valladolid AEMET shows higher errors than other stations, although this might be because the station has the largest database. The effect of climatic zone is similar to the case of hourly values, showing the worst agreement in climatic zones I and IV. Lindfors et al. [2007] compared reconstructed and Table 4. Mean Value of the Ratio of Estimated to Measured 10 min UVER Irradiance for Different SZA Intervals for the Valladolid SRS Station a SZA Interval Season 20 ± 5 30 ± 5 40 ± 5 50 ± 5 60 ± 5 70 ± 5 80 ± 5 Spring 1.02 (0.08) 1.01 (0.06) 1.01 (0.06) 1.00 (0.13) 0.98 (0.15) 0.96 (0.19) 0.91 (0.21) Summer 1.02 (0.06) 1.00 (0.05) 0.99 (0.06) 0.98 (0.08) 0.96 (0.11) 0.93 (0.22) 0.92 (0.35) Autumn 0.99 (0.05) 0.99 (0.07) 0.98 (0.08) 0.97 (0.11) 0.94 (0.13) 0.90 (0.18) Winter 0.97 (0.04) 0.95 (0.12) 0.95 (0.17) 0.97 (0.22) All 1.02 (0.06) 1.01 (0.06) 1.00 (0.06) 0.99 (0.10) 0.97 (0.12) 0.94 (0.18) 0.93 (0.26) a The standard deviation of the ratio is given in parentheses. 8of15

9 Figure 6. Comparison of measured and estimated 10 min (Valladolid SRS) and hourly (AEMET) solar erythemal UV irradiance values for the model UVER RECO at eight stations analyzed in Spain. The enhancement data relationship is also shown for Valladolid (SRS station) in Figure 6b. Figures 6a and 6c only show 10% of their total data due to the high number thereof. Dashed lines mean 1:1 line, and solid ones are the linear fits. measured daily UVER values for four stations in northern Europe and always obtained the highest rmse values in winter (similar results to this paper). In addition, Lindfors et al. [2007] found rmse values ranging from 5% in summer (Norrköping and Sodankylä stations in Sweden and Finland, respectively) to 22% in winter (Jokioinen station in Finland). Four reconstruction methods were developed by Rieder et al. [2008], which were tested with daily UVER measurements at Sonnblick and Vienna (Austria), showing rmse values higher than 10% for all seasons. Rieder et al. [2010] compared reconstructed and observed daily UVER doses on days with high erythemal UV doses for Davos (Switzerland), Sonnblick, and Vienna in , the obtained rmse values being 8%, 10%, and 12%, respectively, which are in general higher rmse values than those obtained in this work (Table 7) for all conditions (not only high erythemal UV doses). After studying these results, it can be concluded that the model is valid and that the greatest accuracy is achieved for high solar radiation levels. [45] In consequence, the proposed UVER RECO model can be seen to provide good results. It will enable erythemal UV irradiance data series to be extended back in time, one Table 5. The rmse, mbe, mabe, Number of Data, and Determination Coefficient for 10 min Erythemal Irradiance Model, UVER RECO, for Valladolid SRS Station Place Season rmse (mw m 2 ) mbe (mw m 2 ) mabe (mw m 2 ) N r 2 Valladolid (SRS) Spring 4.6 (6.4%) 0.2 ( 0.3%) 3.2 (4.5%) 11, Summer 6.4 (6.0%) 0.5 ( 0.4%) 4.5 (4.2%) 13, Autumn 4.7 (8.3%) 1.7 ( 3.0%) 3.2 (5.7%) 9, Winter 3.2 (13.1%) 1.5 ( 6.2%) 2.3 (9.3%) 5, All 5.2 (7.0%) 0.8 ( 1.1%) 3.5 (4.8%) 39, Enhancement 12.1 (9.8%) 3.7 (6.4%) 8.9 (7.2%) of15

10 Table 6. The rmse, mbe, mabe, Number of Data, and Determination Coefficient for Hourly Erythemal Irradiance Model, UVER RECO, for Seven AEMET Stations Place Season rmse (mw m 2 ) mbe (mw m 2 ) mabe (mw m 2 ) N r 2 A Coruña Spring 6.8 (8.6%) 3.2 ( 4.0%) 5.2 (6.5%) 1, Summer 11.2 (9.2%) 7.3 ( 6.0%) 8.6 (7.0%) 1, Autumn 5.2 (9.5%) 2.5 ( 4.5%) 3.4 (6.3%) Winter 2.9 (12.4%) 1.1 (4.7%) 2.1 (8.9%) All 7.5 (10.2%) 3.2 ( 4.4%) 5.0 (6.9%) 3, Granada Spring 5.7 (5.2%) 0.0 (0.0%) 4.4 (4.1%) 1, Summer 8.1 (4.7%) 1.0 ( 0.5%) 6.3 (3.6%) 1, Autumn 5.7 (8.6%) 0.9 ( 1.5%) 4.1 (6.0%) Winter 3.7 (10.0%) 1.9 (5.2%) 2.8 (7.5%) All 6.1 (6.1%) 0.0 (0.0%) 4.5 (4.5%) 4, Madrid Spring 10.3 (10.4%) 3.3 ( 3.3%) 6.8 (6.8%) 1, Summer 8.8 (5.6%) 3.0 ( 1.9%) 7.1 (4.5%) 1, Autumn 5.5 (8.9%) 0.2 ( 0.4%) 4.0 (6.3%) 1, Winter 4.3 (14.4%) 0.8 (3.0%) 2.8 (9.2%) All 7.8 (8.6%) 1.6 ( 1.7%) 5.3 (5.9%) 4, Murcia Spring 7.4 (7.3%) 3.7 (3.7%) 5.3 (5.3%) 1, Summer 10.1 (6.7%) 6.1 (4.1%) 7.4 (4.8%) 1, Autumn 4.7 (7.6%) 2.4 (3.9%) 3.3 (5.3%) 1, Winter 3.3 (10.0%) 2.3 (7.0%) 2.6 (8.0%) 1, All 7.0 (7.9%) 3.7 (4.2%) 4.8 (5.4%) 4, León Spring 6.1 (5.4%) 2.6 ( 2.3%) 4.9 (4.3%) Summer 7.7 (4.9%) 2.9 ( 1.9%) 6.0 (3.8%) Autumn 4.2 (6.3%) 1.6 ( 2.4%) 3.2 (4.8%) Winter 2.7 (10.0%) 0.2 (0.7%) 2.1 (7.7%) All 5.6 (6.0%) 1.8 ( 1.9%) 4.1 (4.4%) 1, Valladolid AEMET Spring 6.2 (8.4%) 0.8 ( 1.0%) 4.2 (5.6%) 10, Summer 9.1 (8.3%) 1.0 (1.0%) 6.2 (5.6%) 11, Autumn 5.1 (9.4%) 1.5 (2.8%) 3.5 (6.3%) 8, Winter 3.1 (11.1%) 1.0 (3.6%) 2.3 (8.6%) 5, All 6.8 (9.1%) 0.6 ( 0.8%) 4.4 (5.0%) 35, Zaragoza Spring 6.1 (6.1%) 3.4 ( 3.4%) 4.8 (4.8%) 1, Summer 7.6 (4.9%) 6.2 ( 0.4%) 5.8 (3.7%) Autumn 4.8 (7.6%) 0.8 ( 1.4%) 3.1 (4.9%) Winter 2.2 (7.6%) 0.15 (0.4%) 1.6 (5.6%) All 5.6 (6.4%) 1.3 ( 1.5%) 3.9 (4.4%) 3, of the World Health Organization s objectives, and will be useful for evaluating accumulated effects of solar erythemal UV irradiance in the biosphere and impact on human health Reconstructed Data Analysis [46] The UVER RECO model was tested using measurements from the Valladolid AEMET station for the period and showed good results. Hourly UVER irradiance data were thus reconstructed using SW and TOC values, and a data set with 68,516 data from 1991 to 2010 was obtained. [47] A total of 6871 daily UVER irradiation values was obtained by integrating reconstructed hourly UVER irradiance. Monthly UVER values were calculated as the averages of the daily irradiation data in each month, taking into account at least 20 values per month, giving 231 months with data. Seasonal values were the average among their three corresponding months, and the yearly values were considered as the average of the monthly mean UVER irradiation. Yearly values were calculated only if the year had 12 monthly values. This criterion provided 14 yearly UVER values. [48] Analysis of the reconstructed series was based on monthly and yearly data, such that a comparison between monthly and yearly modeled data and measurements was therefore necessary. Table 8 was obtained using monthly UVER measurements from 2000 to In this case, the criterion about requiring at least 20 days was not necessary since averages were carried out with the same days. The agreement is good, with the best results appearing in summer and spring with rmse values below 5%. In addition, the percentage of reconstructed monthly data presenting an absolute deviation lower than 10% was 93%, while Lindfors et al. [2007] and Rieder et al. [2008] reported percentages of 75%, 89%, 90%, 97%, 96%, and 67% in Jokioinen, Norrköping, Bergen, Sodankylä, Sonnblick, and Vienna, respectively. 97% of the reconstructed monthly UVER values in Valladolid AEMET are found within ±10% of the measured ones in summer, a percentage that rises to 100% in spring. Moreover, when compared with measurements, yearly values showed an rmse and mbe below 4% and 1%, respectively. The analysis concludes that reconstructed data series can be used to reproduce past UVER data accurately. [49] In order to study the temporal trend, Figure 7 shows the temporal evolution of reconstructed UVER irradiation as a function of the year for different seasons. In addition, the temporal evolutions for TOC and SW irradiation were included in Figure 7. The solid straight line is the trend over the past 20 years, and the values of these trends are shown in Table 9. Statistically significant UVER trends appeared in summer and autumn when UVER levels increased 3.5% and 4.1% per decade, respectively. In summer, the TOC trend is 10 of 15

11 Table 7. The rmse, mbe, mabe, Number of Data, and Determination Coefficient for Daily Erythemal Irradiation Model, UVER RECO, for Valladolid SRS and Seven AEMET Stations Place Season rmse (J m 2 ) mbe (J m 2 ) mabe (J m 2 ) N r 2 A Coruña Spring 145 (7.4%) 79 ( 6.0%) 201 (6.6%) Summer 238 (7.8%) 182 ( 7.0%) 200 (6.6%) Autumn 96 (7.5%) 58 ( 4.9%) 71 (5.5%) Winter 52 (10.9%) 23 (4.7%) 36 (7.6%) All 151 (8.8%) 75 ( 4.4%) 108 (6.2%) Granada Spring 111 (3.6%) 1.1 ( 0.1%) 87 (2.8%) Summer 170 (3.4%) 27 ( 0.5%) 136 (2.7%) Autumn 112 (6.3%) 28 ( 1.5%) 91 (4.8%) Winter 76 (7.8%) 50 (5.2%) 59 (6.9%) All 123 (4.4%) 1.2 ( 0.1%) 94 (3.4%) Madrid Spring 200 (7.2%) 92 ( 3.3%) 141 (5.0%) Summer 183 (4.1%) 86 ( 1.9%) 146 (3.2%) Autumn 88 (5.1%) 7 ( 0.4%) 65 (3.8%) Winter 66 (9.2%) 21 (3.0%) 39 (5.5%) All 149 (6.1%) 42 ( 1.7%) 99 (4.0%) Murcia Spring 177 (6.3%) 106 (3.7%) 133 (3.7%) Summer 235 (5.4%) 176 (4.1%) 189 (4.4%) Autumn 107 (6.1%) 68 (3.9%) 79 (4.5%) Winter 72 (8.6%) 59 (7.0%) 6.1 (7.3%) All 161 (6.6%) 103 (4.2%) 117 (4.8%) León Spring 114 (4.1%) 62 ( 2.2%) 92 (3.3%) Summer 133 (3.4%) 76 ( 1.9%) 102 (2.6%) Autumn 73 (4.6%) 39 ( 2.5%) 55 (3.5%) Winter 41 (7.1%) 4 (0.7%) 31 (5.3%) All 99 (4.4%) 43 ( 1.9%) 71 (3.2%) Valladolid AEMET Spring 174 (6.2%) 28 ( 1.0%) 127 (4.5%) Summer 299 (6.4%) 45 (1.0%) 233 (5.0%) Autumn 130 (7.8%) 46 (2.8%) 88 (5.3%) Winter 63 (10.4%) 22 (3.6%) 44 (7.3%) All 189 (7.6%) 21 (0.8%) 125 (5.0%) 3, Valladolid SRS Spring 86 (3.0%) 8 ( 0.3%) 65 (2.2%) Summer 163 (3.4%) 21 ( 0.4%) 125 (2.6%) Autumn 97 (5.6%) 52 ( 3.0%) 71 (4.1%) Winter 57 (9.4%) 38 ( 6.2%) 44 (7.1%) All 112 (4.2%) 30 ( 1.1%) 80 (2.9%) Zaragoza Spring 133 (5.2%) 88 ( 3.4%) 106 (4.1%) Summer 152 (3.8%) 16 ( 0.4%) 0.12 (2.9%) Autumn 78 (5.0%) 22 ( 1.4%) 55 (3.6%) Winter 34 (5.1%) 3 (0.3%) 25 (3.7%) All 110 (5.0%) 32 ( 1.5%) 77 (3.5%) not statistically significant, although SW irradiation does increase 4.4% per decade, evidencing a statistically significant trend, called brightening. Wild et al. [2005] found a dimming (negative trend) in SW radiation for the period and brightening in the 1990s with a positive trend at different world sites, concurring with the results obtained at the Valladolid AEMET station. Wild et al. [2005] observed brightening in SW for both cloud free and all sky data sets, and concluded that positive trends in SW radiation for the 1990s were caused by two factors: an increase in the transparency of the cloud free atmosphere that may be related to a fall in aerosol burden and a decrease in cloudiness after the late 1980s, found by Rossow and Dueñas [2004]. A reduction in cloudiness explains the increase in UVER levels and a stronger trend in SW than in UVER since SW radiation is more sensitive to clouds. Moreover, the UVER trend is lower than the SW trend because of a (not statistically significant) increase in TOC during the period, as a 1% rise in TOC causes a UVER reduction of around 1.1% according to the results of de Miguel et al. [2011] in the nearby Valladolid SRS station. In autumn, the UVER trend is also positive because of clouds, as the SW trend is significant and positive. However, the autumn trend might be affected by the first data Table 8. The rmse, mbe, mabe, Number of Data, and Determination Coefficient for Monthly Erythemal Irradiation Model, UVER RECO, for Valladolid AEMET Station Place Season rmse (J m 2 ) mbe (J m 2 ) mabe (J m 2 ) N r 2 Valladolid (AEMET) Spring 121 (4.3%) 28 ( 1.0%) 101 (3.6%) Summer 223 (4.8%) 46 (1.0%) 189 (4.0%) Autumn 92 (5.5%) 47 (2.8%) 66 (4.0%) Winter 50 (8.2%) 18 (2.9%) 36 (5.9%) All 138 (5.6%) 20 (0.8%) 98 (4.0%) of 15

12 Figure 7. Temporal evolution of seasonal reconstructed UVER irradiation, measured SW irradiation and TOC values for different seasons, at the Valladolid AEMET station, Spain, during the period The solid gray straight line indicates the linear fit taking into account the two decades. Dashed black lines are the linear fit for each decade. point (1991) in the time series as data in the following two years are missing (1992 and 1993). In this season, a statistically significant positive trend in TOC appears, which means a reduction in UVER levels, although the cloud reduction effect is higher and leads to a positive UVER trend. The opposite is the case when the yearly (all seasons) values are analyzed. TOC and SW showed a positive and statistically significant trend, which means an increase in UVER that is due to clouds and a decrease that is due to ozone changes, both providing a nonstatistically significant trend in UVER. [50] Figure 7 and Table 10 show trends of UVER, SW, and TOC separately during the periods and A statistically significant increase appears in SW irradiation in summer and autumn over the last decade and a decreasing trend in yearly UVER and SW from 1991 to Yearly evolution can be seen in detail in Figure 8. [51] The results obtained were compared with previous studies. Lindfors et al. [2007] found a similar significant Table 9. Statistical Parameters of UVER, SW, and TOC Trends for Different Seasons Along the Period a Season Quantity N Trend (% yr 1 ) r 95% Confidence Interval for r Spring UVER ± ( 0.52, 0.36) SW ± ( 0.31, 0.56) TOC ± ( 0.15, 0.66) Summer UVER ± ( 0.05, 0.73) SW ± (0.08, 0.79) TOC ± ( 0.21, 0.63) Autumn UVER ± ( 0.08, 0.73) SW ± ( 0.01, 0.76) TOC ± (0.02, 0.75) Winter UVER ± ( 0.74, 0.13) SW ± ( 0.31, 0.65) TOC ± ( 0.05, 0.72) All UVER ± ( 0.51, 0.55) SW ± ( 0.08, 0.80) TOC ± (0.04, 0.76) a The statistically significant trends are indicated in bold font. 12 of 15