Role of aerosols on the Mediterranean solar radiation - ocean

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Ajaccio (France). 41°550N 8°480E. 77610. 6. 1970–1993 ..... Res., 99, 5119–5134, 1994. Gupta, S. K., N. A. Ritchey, A. C. Wilber, C. H. Whitlock, G. G. Gibson,.
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. C2, 3025, doi:10.1029/2001JC001258, 2003

Role of aerosols on the Mediterranean solar radiation Elina Tragou Institute of Oceanography, National Centre for Marine Research, Athens, Greece

Alex Lascaratos Department of Applied Physics, University of Athens, Athens, Greece Received 12 December 2001; revised 22 March 2002; accepted 2 April 2002; published 11 February 2003.

[1] The shortwave radiation constitutes a major driving force for the oceanic thermohaline

circulation and must be accurately parameterized. An extensive comparison between the monthly mean solar radiation measured at numerous meteorological stations along the Mediterranean coast and the estimated solar radiation from a widely used bulk formula leads to two important conclusions: first, there is systematic overestimation of the shortwave radiation from the bulk formula at the station sites of about 25 W m2 (averaged over a 30 year period), and second, there is significant interannual variability in the observed solar radiation that cannot be explained either by the variability in the available data for the cloudiness or by the absorption due to water vapor content in the atmospheric column. As the overestimation occurs during summer cloud-free months, we assume that cloud attenuation is adequately parameterized and the discrepancy between the observations and the estimates comes from the formula for clear-sky insolation. A correction to the clear-sky insolation formula is attempted on the basis of recent satellite data on aerosol optical thickness index. Results from our analysis indicate that aerosols may provide an explanation for both the observed weaker shortwave radiation and its interannual variations. This implies that a different parameterization scheme must be INDEX TERMS: 4504 sought for the aerosol attenuation in the shortwave radiation formula. Oceanography: Physical: Air/sea interactions (0312); 3359 Meteorology and Atmospheric Dynamics: Radiative processes; 4215 Oceanography: General: Climate and interannual variability (3309); 3360 Meteorology and Atmospheric Dynamics: Remote sensing; KEYWORDS: Mediterranean solar radiation, bulk formula correction, aerosol attenuation Citation: Tragou, E., and A. Lascaratos, Role of aerosols on the Mediterranean solar radiation, J. Geophys. Res., 108(C2), 3025, doi:10.1029/2001JC001258, 2003.

1. Introduction [2] The amount of shortwave radiation reaching the ocean surface is important in physical oceanography as it is a major boundary forcing for the simulations of the oceanic circulation and a crucial parameter for the estimates of the water mass formation rates. Despite its significance, it remains a very poorly recorded quantity; solar radiation over the ocean is usually estimated either from empirical parameterizations using the commonly recorded cloudiness and the noon solar elevation [e.g., Budyko, 1974; Reed, 1977], or from radiative transfer models using satellite records [e.g., Bishop et al., 1997; Gupta et al., 1999]. Because of the duration and the simplicity of the former they are still widely used in oceanography and climate modeling, although it is well known that such parameterizations often introduce systematic errors [e.g., Dobson and Smith, 1988]. Copyright 2003 by the American Geophysical Union. 0148-0227/03/2001JC001258$09.00

[3] In semienclosed basins such as the Mediterranean Sea there is a simple and efficient method to test the validity of data sets and bulk parameterizations for the surface heat fluxes. This involves the comparison of the surface heat budget with the known advective heat flux through the Strait of Gibraltar. Indeed, the restricted connection of the Mediterranean with the open ocean allows for a relatively accurate estimate of the oceanic advective heat flux. The latest estimate is 5 ± 1 W m2 [Macdonald et al., 1994]. Several researchers [e.g., Bunker et al., 1982; Garrett et al., 1993; Gilman and Garrett, 1994] have examined in the past the validity of the estimates for the air-sea fluxes in the Mediterranean Sea using this constraint as a guide. Significant differences between the estimated surface fluxes and these implied by the exchange through the strait are systematically found. In previous studies, the differences were explained by the overestimation of the shortwave radiation and/or the underestimation of the longwave, latent and sensible heat fluxes. [4] In particular, the basin average, long-term mean total shortwave radiation in the Mediterranean was first estimated

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Table 1. Previous Estimates of the Mediterranean Solar Radiation Reference Bunker et al. [1982] Garrett et al. [1993] Gilman and Garrett [1994] Castellari et al. [1998]

Formula

Period

Qs, W m2

Budyko Seckel & Beaudry and Reed Seckel & Beaudry and Reed List and Reed

1941 – 1972 1945 – 1989

202 202

1945 – 1989

183

1980 – 1988

202

by Bunker et al. [1982]. They used the Budyko [1963] formula including the albedo from Payne [1972] and ships’ observations for the years 1941 – 1972 from the U.S. National Climate Center. The estimated solar radiation by Bunker et al. [1982] was 202 W m2, although a reduction of about 5 W m2 (or 2.5%) due to aerosols was considered plausible. [5] A later estimate by Garrett et al. [1993] using the Comprehensive Ocean Atmosphere Data Set (COADS) [Woodruff et al., 1987] for the 45 year period 1945 – 1989 and the formula of Seckel and Beaudry [1973] with Reed’s [1977] cloud formula, originally gave Qs = 202 W m2, same as the Bunker et al. estimate. Garrett et al. suggested that this is an overestimation, possibly due to the neglected effect of aerosols, and drew attention on the uncertainty in the interannual changes of Qs estimated from atmospheric radiation formulas based on cloud reduction alone. In the extreme hypothetical case where all of the difference between the known and the estimated heat budget comes from the overestimation of the insolation, a reduction of 36 W m2 (or 18%, i.e., Qs = 166 W m2) must be applied to balance the budget. [6] In an attempt to objectively quantify this reduction Gilman and Garrett [1994] concluded that the correct usage of the Reed cloud formula resulted in a decrease of 13 W m2 (or 6.5%). A further decrease of about 6 W m2 (or 3%) was estimated due to a seasonally varying transmission coefficient, introduced to match measurements of a ground station in Cyprus. Overall, their corrections to the solar radiation formula resulted in a value of Qs = 183 W m2. [7] The overestimation of the solar radiation from the bulk formula in the Mediterranean was also demonstrated by Schiano [1996], who was the first to compare the formula with direct marine measurements of Qs during several cruises in the Western Mediterranean in the period 1989 – 1994. She showed that Reed’s cloud formula performed reasonably well under cloudy conditions, but under clear sky there was a systematic overestimation of the estimated insolation. She also pointed out the role of the varying water vapor density in attenuating the solar radiation. [8] Finally, for completeness, we mention that Castellari et al. [1998] reestimated the solar radiation using the same formula and data set as Garrett et al. [1993] and Gilman and Garrett [1994] but for the 9 year period 1980 – 1988 and found a value of 202 W m2. The above results are summarized in Table 1. [9] Because of its geographic location and the regional weather systems, the atmosphere over the Mediterranean has high aerosol concentration [Gilman and Garrett, 1994], both mineral and anthropogenic, as well as high water vapor

content [Schiano, 1996]. Both aerosols and water vapor attenuate the incoming shortwave radiation, but are parameterized with constant coefficients in the clear-sky insolation formula, which possibly overestimates the insolation. [10] In general, previous studies agree that Qs is overestimated by the bulk formula in the Mediterranean, because of the neglect of the seasonal and spatial variability of aerosols and water vapor density. The attempts by Gilman and Garrett [1994] to quantify the bias were based on a fit to the observations from only one station in the eastern Mediterranean for a 4 year period. Schiano’s [1996] very interesting comparisons with direct ship observations were in the Western Mediterranean and only for 88 days over a period of 6 years. A systematic long-term basin-scale comparison of the formula estimates with the observations is lacking in the Mediterranean. Taking into account the peculiarities of the Mediterranean area a correction to the formula for radiation is needed. [11] In this paper we present the results of a comparison between direct measurements of solar radiation at several coastal meteorological stations during a 30 year period and the estimates from a commonly used bulk formula. To account for the spatial and temporal variability of aerosols over the whole Mediterranean we have used satellite data for the aerosol optical thickness calibrated at the ground stations and attempted to correct the Qs estimates. In section 2, we present bulk formula estimates of solar radiation in the Mediterranean, and in section 3, the ground records. The satellite data are described in section 4 and used in the corrective method which is introduced in section 5. Our results are discussed in section 6.

2. Bulk Formula Estimates [12] The downward component of the insolation Qs at the sea surface is often calculated from the formula Qs ¼ QCS ð1  cn n þ 0:0019hÞ:

ð1Þ

This is a combination of the cloud reduction formula from Reed [1977], originally derived for daily cloud cover values, and the formula for the clear-sky irradiance QCS from List [1958]. The latter comes from the sum of direct and scattered solar radiation. This is  1  QCS ¼ Q0 Tr1=cos z þ ð1  AÞ ; 2

ð2Þ

where Q0 is the incident radiation at the top of the atmosphere (Q0= S0 cos z, with S0 = 1370 W m2 the solar constant, and z the solar zenith angle), Tr is the transmission factor for a clear atmosphere (held constant at 0.7), and A = 0.09 is the absorption factor due to ozone and water vapor. In equation (1) the reduction due to clouds is parameterized, following Reed [1977], by the product of the cloud fraction n and a coefficient cn = 0.62; h is the noon solar altitude in degrees. [13] Reed [1977] pointed out that when the cloud cover fraction is smaller than 3 octas the calculated solar radiation Qs from equation (1) is larger than the clear-sky irradiance QCS and an overestimation of Qs is introduced. Therefore, the cloud reduction formula should be truncated at 1; this is

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Figure 1. Long-term mean net shortwave radiation Qs estimated from equation (1) with the cloud correction and cloudiness from UWM/COADS.

applied in our estimates. Moreover, it should be noted that Reed’s cloud reduction formula was derived for estimating daily means of solar radiation, therefore the effects of using monthly instead of daily values are ignored in our estimates. These effects are discussed later in the paper. Reed’s formula was also derived using another formula for the clear-sky radiation by Seckel and Beaudry [1973], though Schiano [1996] showed that this formula for the clear-sky radiation is not appropriate for the Mediterranean Sea conditions. [14] We note here that previous estimates of Qs mentioned in Table 1 are computed using the formula Qs ¼ QCS ð1  cn n þ 0:0019hÞð1  aÞ;

ð3Þ

which gives the total shortwave radiation budget at sea surface, i.e., the downward minus the upward component. In our analysis we will use estimates from equation (1) because they are comparable to the observed global radiation. [15] An up-to-date estimate of the solar radiation from the formula is possible by extracting the cloudiness values over the Mediterranean Sea from the revised COADS by da Silva et al. [1994a, 1994b, 1994c, 1994d, 1994e] at the Department of Geoscience of the University of Wisconsin-Milwaukee (henceforth referred to as UWM/COADS). This data set covers the 49 year period from January 1945 to December 1993 and comes from the original COADS release objectively analyzed on a 1  1 global grid. The data set includes corrections to the wind speed, cloud cover, and Present Weather observations, as well as global estimates for the four components of the heat budget evaluated using standard bulk formulas. [16] We have calculated Qs using equation (1) and the monthly cloud cover fraction from UWM/COADS. For the Mediterranean Sea the Qs average is 218 W m2. This value includes a small decrease (of about 3 W m2) due to the correct application of the Reed cloud formula, i.e., truncated for small cloudiness values in order to obtain solar radiation always less than the clear-sky insolation. However, monthly rather than daily cloud values have been used in our

estimates, which, according to Gilman and Garrett [1994], can lead to an overestimation of about 4%. The spatial distribution of the 49 year mean of this new estimate is presented in Figure 1 which shows, as expected, a northsouth gradient in Qs. [17] Results from formula (1) have been compared in the past against ground truth observations to test the performance of this bulk formula in the Mediterranean conditions. For example, Schiano [1996] used short-term data from ship’s records during various cruises, while Gilman and Garrett [1994] used a few years of data at a single site in Cyprus. Still, there are no long-term, large-scale comparisons that would give a statistically robust result. The results of these previous comparisons will be discussed later in the paper. At this point, to check the validity of the bulk formula (equation (1)) we compare our estimates with an extensive data set of ground truth observations for Qs presented in the following section.

3. Ground Truth Observations [18] Observations of surface solar irradiance are available from the World Radiation Data Centre (WRDC) at numerous meteorological stations along the Mediterranean coast. These include monthly mean measurements of Qs under any condition (both cloudy and clear sky) from January 1964 to December 1994 at the locations shown in Figure 2. These 23 stations have been selected from a larger group of 83 stations, so that the selected stations are evenly distributed over the whole Mediterranean in order to reduce the bias of a nonuniform distribution; there are several stations available along the northern coast of the Mediterranean, but very few along the North African coast. The specific stations were also selected for their duration of observations and their elevation above sea level (which is less than 135 m and only in one case at 191 m). [19] Details about the data available at each station are presented in Table 2. The table includes information about the station location, elevation above sea level, the period of observations, the number of monthly data available at each

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Figure 2. Locations of the selected 23 World Radiation Data Centre stations along the Mediterranean coast.

station, the mean Qs and the standard deviation, as well as the mean Qs of contemporaneous estimated values (using formula (1) and cloudiness data from UWM/COADS) and their difference. We note that for all of the estimates of Qs we have allowed for a truncation to Reed’s formula for small cloudiness values. We compared the WRDC observations with bulk formula estimates at the grid points overlapping with or nearest to the coastal stations. The difference of the estimates from the measurements is positive at all but one station, indicating that there is overestimation in Q s from the bulk formula. The mean difference is about 25 W m2. [20] All station matches (around 5300 data pairs) are shown in the scatter plot of Figure 3. We found that around 3900 data pairs of monthly mean values show overestimation of Qs, mostly for high Qs values, of as much as 100– 150 W m2. There are also around 1400 data pairs, which show a slight underestimation, mostly for low Qs values. [21] The time series of measured and estimated values of Qs are shown in Figure 4 for four indicative stations. The overestimation of the solar radiation estimated with formula (1) is evident at all four stations. [22] We have also estimated the mean time series from all the selected 23 stations shown in Figure 5a. The solid line corresponds to the mean values of all the available measured values for each month, and the dashed line corresponds to the mean value of the estimated Qs (included only if the contemporaneous measured value is available) for each month, at grid cells next to the stations which have data. It should be noted that the insolation from formula (1) is found to be systematically larger than Qs observed at the WRDC stations throughout the low-pass filtered time series. This indicates that some attenuating processes are not properly parameterized in the bulk formula. Moreover, it is obvious that there is strong interannual variability in the observed Qs compared to the estimated insolation. The seasonal signal shows that the summer values are overestimated whereas there is a small underestimation in the

winter solar radiation. We also note that data are not always available for all 23 stations during the 30 year period under examination. The number of observations for each month is presented in Figure 5d, which shows that there is shortage of data during the last pentad. [23] The fact that Figure 5a shows similar results as the randomly chosen stations in Figure 4 is a strong indication that, statistically, the reason for the discrepancies between the estimates and the records is the bulk formula, not the possible errors in the records. We also note that the mean solar radiation from the 23 stations is very similar to the mean solar radiation obtained from all the 83 stations (not shown here). [24] The 30 year mean of the estimated Qs at all stations is 210 W m2, whereas the observed Qs is 185 W m2, smaller by about 25 W m2. This discrepancy can be attributed to systematic errors due to erroneous parameterizations for the various attenuating factors (such as aerosols, water vapor, and other atmospheric molecules) in the clearsky formula (2), and/or to systematic errors in the cloud cover observations and the cloud reduction formula. [25] For the cloud reduction in particular, Schiano [1996] showed that Reed’s [1977] formula performs reasonably well in the Mediterranean, though it is possible that there are errors in the available data for the cloud cover. Nevertheless, Reed’s formula applies only for cloudiness greater than approximately 3 octas (equivalent to 0.38 cloud fraction [Reed, 1977]). The basin-averaged 49 year mean cloudiness from the UWM/COADS over the whole Mediterranean is 0.42, close to the lower limit of 0.38; the eastern Mediterranean which occupies the greatest part of the basin has a mean cloudiness of 0.40, and the Western Mediterranean has 0.47. Moreover, Figures 4 and 5a clearly show that the greatest differences between the observed and the estimated solar radiation exist during summer months when cloud cover is very small in the Mediterranean, below the limit of 3 octas. Therefore we will assume that any difference between the observations and the estimates comes

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Table 2. Comparison of Measured Solar Radiation From the WRDC and the Estimated Values Using the Bulk Formula (1) and Cloudiness From UWM/COADS

Index Number

Location

1

Dar el Beida (Alger) 36430N 3150E Mersa Matruh (Egypt) 31200N 27130E Ajaccio (France) 41550N 8480E Nice (France) 43390N 7120E Perpignan (France) 42440N 2520E Andravida (Greece) 37550N 21170E Athens Obs.(Greece) 37580N 23430E Tymbakion (Greece) 35000N 24450E Bet Dagan (Israel) 32000N 34490E Alghero (Italy) 40380N 8170E Amendola (Italy) 41320N 15430E Brindisi (Italy) 40390N 17570E Genova/Sestri (Italy) 44250N 8510E Messina (Italy) 38120N 15330E Napoli (Italy) 40510N 14180E Pantelleria Is. (Italy) 36490N 11580E Roma/Ciampino (Italy) 41480N 12330E Beyrouth Aprt (Lebanon) 33490N 35290E Qrendi, Malta Is. (Malta) 35500N 14260E Murcia (Spain) 38000N 1100E Palma de Mallorca (Spain) 39330N 2370E Sidi Bouzid (Tunisia) 36520N 10210E Bar (Yugoslavia) 42060N 19060E

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Station Code

Elevation, m

Period of Observations

Number of Monthly Data

Mean ± Std Observations

Mean ± Std UWM/COADS

Difference

603900

25

1970 – 1975

62

194.6 ± 75.7

215.2 ± 97.9

20.6

623060

25

1981 – 1993

144

229.9 ± 79.6

227.1 ± 98.2

2.8

77610

6

1970 – 1993

271

175.5 ± 83.2

193.6 ± 101.8

18.0

76900

4

1967 – 1993

288

167.3 ± 79.6

184.3 ± 100.4

17.0

77470

43

1980 – 1993

154

162.2 ± 73.0

189.2 ± 99.2

27.0

166820

17

1978 – 1993

134

176.6 ± 92.1

217.4 ± 108.4

40.8

167140

107

1964 – 1986

264

181.9 ± 80.7

217.7 ± 107.3

35.8

167590

7

1977 – 1993

133

159.6 ± 73.3

231.1 ± 100.5

71.5

401790

30

1964 – 1978

164

224.5 ± 76.8

243.5 ± 93.4

19.1

165200

23

1964 – 1989

298

189.1 ± 86.9

196.6 ± 101.8

7.5

162610

57

1964 – 1993

356

182.2 ± 82.8

200.5 ± 104.7

18.3

163200

15

1964 – 1993

357

172.8 ± 86.1

204.8 ± 106.3

32.0

161200

2

1964 – 1989

304

149.9 ± 74.8

186.4 ± 100.5

36.5

164200

59

1964 – 1993

348

184.8 ± 81.8

210.8 ± 104.5

26.0

162890

88

1964 – 1989

293

173.1 ± 82.9

202.9 ± 104.4

29.8

164700

191

1964 – 1993

301

184.4 ± 79.3

219.2 ± 101.1

34.9

162390

129

1964 – 1993

294

179.8 ± 81.5

201.2 ± 102.7

21.4

401000

29

1964 – 1981

157

192.3 ± 75.0

226.4 ± 96.6

34.1

165960

135

1964 – 1978

105

210.2 ± 81.8

220.5 ± 101.6

10.3

84300

61

1981 – 1993

139

199.0 ± 74.9

210.9 ± 94.1

11.9

83010

6

1981 – 1993

127

182.1 ± 77.4

198.3 ± 97.0

16.2

607175

127

1968 – 1993

305

199.2 ± 78.7

210.4 ± 101.0

11.2

134610

4

1964 – 1991

306

178.3 ± 85.4

199.5 ± 106.2

21.2

from the clear-sky formula only. Further discussion on this assumption will be provided in section 6. [26] Gilman and Garrett [1994] first showed that for the Mediterranean insolation there is a systematic error in the parameterization of atmospheric attenuation in the clear-sky radiation formula. The transmission coefficient Tr, which is used to parameterize extinction due to absorption and scattering by aerosols and other atmospheric constituents, is assumed to be uniform and constant. However, concentrations of natural and anthropogenic aerosols may vary both in space and time so that considering aerosol effects as constant may lead to systematic errors in the Qs estimate for certain areas of the world. The Mediterranean Sea appears to be in such a region with high aerosol load. This is evident from the global distributions of the aerosol optical thickness index detected by polar orbiting satellites (e.g., AVHRR

Pathfinder) over the oceans, first presented by Husar et al. [1997], who also showed that there is significant seasonal variability of the aerosol concentration in the atmosphere. This currently available aerosol optical thickness index cannot be simply related to the transmission coefficient Tr of clear-sky insolation, because the data do not provide information about aerosol properties (i.e., size, shape, and composition), and lack the required calibration [Lacis and Mishchenko, 1995]. [27] To quantify the attenuation due to aerosols in the Red Sea, Tragou et al. [1999] suggested a simple calibration scheme of satellite measurements of the aerosol extinction index depending on the availability of ground truth observations. Here we will follow a similar approach in the Mediterranean where there is a relatively large number of ground truth measurements of Qs spanning several years

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from coastal meteorological stations provided by the WRDC.

4. Aerosols Over the Mediterranean Sea

Figure 3. Monthly mean values of coastal solar radiation observations from the WRDC versus contemporaneous bulk formula estimates using the UWM/COADS cloudiness.

[28] Sea Satellite data for the index of optical thickness are available online from the U.S. NOAA (National Oceanic and Atmospheric Administration). These include two data sets for the aerosol optical thickness index tsA derived over the ocean from NOAA/AVHRR: The Pathfinder data product for the monthly mean aerosol optical thickness provided on a 1  1 resolution for the period from July 1981 to September 1994 [Stowe et al., 1997]. However, this version of AVHRR aerosol data, despite covering a relatively long period of 14 years, covers only 45% of the Mediterranean area where UWM/COADS data are available. A better coverage is provided by another product of the AVHRR the Aerosol optical thickness (AOT) 100 km field. This data set includes weekly values of AOT computed in near real time, for the period November 1998 to April 2001. The two products are the same in terms of the method used to compute optical thickness from the AVHRR channel 1 reflectance. However, the way in which the two products are gridded is different with the AOT 100 km field sampled at higher densities near the coastal areas. The spatial distribution of the mean AOT from the two products is

Figure 4. Solar radiation time series at four WRDC stations. The dashed line corresponds to the estimated Qs from the List-Reed formula and cloudiness from the UWM/COADS. The solid line corresponds to the observed Qs from the WRDC records. The thin lines are the monthly values, while their thick counterparts are the low-pass filtered time series showing the interannual variations; the thick line scale is on the right axis of the diagram. The filter is a 23-point triangular filter that removes the seasonal cycle.

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Figure 5. Average time series of the observed solar radiation at 23 WRDC stations (solid line) and the contemporaneous values of the estimated solar radiation (dashed line) from (a) original estimates (without corrections), (b) corrected estimates, and (c) estimates using Schiano’s corrections. The line style is same as in Figure 4. (d) Number of observations available at each month of the time series. shown in Figure 6, whereas the time series of the basin average of the two products is shown in Figure 7. We note that the basin average value of the AOT 100vkm field product is lower than the Pathfinder product as the former includes values at areas of lower aerosol load as shown in Figure 6. [29] Figure 7 shows that there is a distinct seasonal and interannual signal which implies that a constant value for the transmission coefficient could be a source of error in the clear-sky insolation estimate. It is also noted that there is a conspicuous peak during 1992, also present in time series from other areas in the world, which is probably related to the eruption of Mount Pinatubo in June 1991. High aerosol load in the atmosphere over the Mediterranean may have caused stronger attenuation to the insolation during those years and may be associated with the so-called Mediterranean transient [Roether et al., 1996; Lascaratos et al., 1999]. However, such a signal of reduced Qs does not appear in the observations, perhaps due to the scarcity of data during that period, so further conclusions are not possible at this stage. [30] In our study the availability of aerosol data near the coastal stations is essential, therefore we have chosen to proceed with the AOT 100 km field, although the possible interannual variability is missing from this data set. We also note that this data set covers a time period where no solar radiation data (either estimates or observations) are available. To overcome this problem we have

averaged the monthly mean values and used a climatological spatially varying seasonal cycle, so that only the seasonal, not the interannual variability of the aerosols is included in our analysis. In the next section we will

Figure 6. Spatial distribution of the mean aerosol optical thickness index from (top) the Pathfinder AOT product and (bottom) the AOT 100 km field.

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Figure 7. Time series of the basin-averaged aerosol optical thickness index in the Mediterranean Sea. The thin line corresponds to the mean value from the Pathfinder AOT product, whereas the thick line corresponds to the AOT 100 km field. attempt to quantify the aerosol effect on the solar radiation of the Mediterranean.

5. Correction to Qs [31] In the Qs formula the effect of aerosols is assumed constant globally. In order to bring the calculated surface solar irradiance (denoted Qs) in agreement with surface observations from WRDC (denoted QsG), we define the transmission anomaly coefficient Tr* as Tr* ¼

QsG : Qs

ð4Þ

This transmission anomaly coefficient is defined by comparing ground measurements QsG with the insolation estimated from equation (1) for the same period of time, at

the 1  1 grid point containing or nearest to the locations of the observations, using cloudiness data from UWM/ COADS. The resulting transmission anomaly coefficient at all stations is shown in Figure 8a. We note that there is a remarkable interannual and seasonal variability with Tr* less than one during summer and slightly greater than one during winter (Figure 8b). This implies that the insolation has been overestimated by the bulk formula during summer and slightly underestimated during winter. [32] The transmission factor Tr* calculated above can be used to calibrate satellite data for the optical thickness index in order to obtain the spatial and temporal variability of Tr* everywhere in the Mediterranean, not only at the station sites. [33] We assume that the satellite data for the index tsA define a transmission coefficient TrsA = exp(tsA/cos z) due to aerosol scattering; aerosol absorption appears in a different term. In order to estimate the clear-sky radiation at the sea surface we introduce a coefficient fc for the attenuation of Q0 due to absorption and scattering by atmospheric molecules and absorption due to aerosols, so that clearsky insolation at the sea surface is given by QCSsat ¼ Q0 fc TrsA :

ð5Þ

[34] We assume that the transmission anomaly coefficient Tr* is only due to errors in the estimation of QCS and there are no errors in the cloudiness or the parameterization scheme for the cloudiness reduction. Then, from equations (1), (2) and (4) the clear-sky insolation estimated from the formula at the coastal stations and corrected by Tr* is given by QCSG ¼ Q0 f Tr* ;

Figure 8. (a) Time series of the transmission anomaly coefficient equation from equation (4) estimated at all WRDC coastal stations in the Mediterranean Sea and (b) its mean seasonal cycle. (c) Calibration factor for the satellite transmission coefficient index estimated from the comparison of the insolation from satellite data with the ground truth records and (d) its mean seasonal signal. Dashed lines are the error bounds for Tr* and fc.

ð6Þ

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Figure 9. Long-term mean of the transmission anomaly coefficient Tr* estimated from equation (7) using data for the satellite transmission coefficient TrsA. where f = 12 (Tr1/cos z + (1  A)). Then, from equations (5) and (6), the relation between the clear-sky radiation estimated from satellite data and the clear-sky radiation from the corrected formula to the ground stations is fc TrsA ¼ f Tr* :

ð7Þ

[35] To calibrate the clear-sky insolation from the satellite data to the measured clear-sky insolation, the coefficient fc must be adjusted so that fc ¼ f

Tr* : TrsA

ð8Þ

At the coastal stations the calibration factor fc is evaluated using the average monthly values of Tr*, the monthly aerosol transmission coefficient TrsA (at the grid points nearest to the coastal stations), and the monthly values of the parameter f. Because of the short duration of TrsA we have used the monthly mean values of the 2.5 years of available data, i.e., one seasonal cycle. The time series of fc estimated at all ground stations and its seasonal cycle is shown in Figures 8c and 8d. We note that the calibration

factor fc includes all the corrections required to match QCS from satellite data to the measured quantities at ground stations. These include the attenuation of Q0 due to absorption and scattering by atmospheric molecules, aerosol absorption, as well as errors in the estimation of TrsA. [36] The estimated fc at all 23 stations has been averaged and the temporally varying calibration factor fc has been used, along with the factor f and the satellite data for TrsA, to estimate the transmission anomaly coefficient Tr* everywhere in the Mediterranean Sea (Figure 9). The spatial distribution of Tr* shows that Qs has been overestimated in areas of high aerosol load. For example, the solar radiation in the Ionian Sea and the Northern Aegean has been overestimated. [37] Figure 10 shows the basin average time series of the estimated Tr* and its seasonal cycle. Besides the small interannual variability in Tr* there is slight underestimation during winter months (Tr* > 1), while there is overestimation during summer. [38] The surface solar radiation obtained with equation (1) is multiplied with this spatially and seasonally varying transmission coefficient Tr*. The resulting corrected QsC = QsTr* is presented in the upper panel of Figure 11. We note

Figure 10. Basin average of the transmission anomaly coefficient Tr* estimated from equation (7) using data for the satellite transmission coefficient TrsA and its seasonal cycle.

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TRAGOU AND LASCARATOS: AEROSOL ROLE ON MEDITERRANEAN SOLAR RADIATION

that, although the north-south gradient of the solar radiation remains similar to that of the originally estimated Qs, the spatial distribution is markedly different with smaller scale features in the long-term mean Qs. In particular, it is worth mentioning that the shortwave radiation is significantly reduced in the Libyan basin. The effect of the correction is more clearly seen in the lower panel of Figure 11, which shows the 30 year mean difference between the original and the corrected Qs over the whole Mediterranean. The correction is greater in the Ionian Sea where the concentration of aerosols is large and near urban areas such as the North Aegean and the North Adriatic Seas. [39] Overall, the 30 year mean (from January 1964 to December 1993) of the basin-averaged corrected estimate is found to be QsTr* = 184 W m2 smaller by 33 W m2 compared to the original estimate of 217 W m2 for the same period.

6. Comparison to Corrected Qs Figure 11. (top) Long-term mean (30 year period) of the corrected shortwave radiation given from QsTr*. (bottom) Long-term mean difference between the original and corrected Qs.

[40] A validation of our correction to the estimated Qs comes from the comparison with the observed values at the coastal stations. Admittedly, the comparison is performed between two data sets that are not independent and the improvement is inevitable. However, we carry out this comparison because the averaging of the calibration factor fc at the 23 stations, and the use of one seasonal cycle of the spatially varying aerosol extinction TrsA introduce errors in our method. These errors are quantified with the following comparison and offer a possible validation of our method.

Figure 12. Solar radiation time series at 4 WRDC stations same as in Figure 4. The line style is same as in Figure 4.

TRAGOU AND LASCARATOS: AEROSOL ROLE ON MEDITERRANEAN SOLAR RADIATION

Figure 13. Difference between the mean value of the original estimates and the observations (solid squares) at each ground station and difference between the mean value of the corrected estimates and the observations (shaded circles), for (top) summer (July), (middle) annual mean, and (bottom) winter (January).

[41] In Figure 12 we present the observed time series at four stations (same as those in Figure 4) compared to the corrected values. The improvement is clearly seen in the seasonal signal. The filtered time series, although improved compared to the original estimates, do not follow exactly the details of the recorded time series. [42] A more thorough comparison of the corrected estimates with the observations is possible from the statistics of each station. In Figure 13 we show the differences of the corrected mean Qs estimates from the mean observed Qs at each station. The three panels correspond to the differences of the mean summer values (Figure 13(top)), annual mean values (Figure 13(middle)), and the winter (Figure 13(bottom)). The root mean square of the differences between the original estimate and the observations at all 23 stations are presented in Table 3. [43] The improvement in the estimates is evident in the annual mean values, but the summer months values are significantly ameliorated compared to the original estimates, whereas there is a negligible deterioration in the winter values. Figure 13 shows that the systematic positive bias in the summer and the annual mean values are significantly reduced, although the corrected summer values show a small negative bias.

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[44] Finally, we present the results from our analysis and corrections to Qs (which have been attributed to aerosol attenuation), and compare them to the original mean observations (Figure 5b). Both the long-term mean and the seasonal and interannual variations are reasonably well represented in the corrected data. [45] As mentioned in section 4, our corrective method is based on the assumption that all the difference between the observed and the estimated solar radiation comes from erroneous parameterization in the clear-sky formula. This assumption is strengthened by the fact that most of the reduction introduced by our method occurs during cloud-free months, i.e., when the cloud reduction formula is redundant. For example, in Figure 14 a decade (1980– 1990) of original and corrected estimates are plotted along with the contemporaneous cloudiness fraction at station 164200 (Messina, Italy). Figure 14 clearly shows that the reduction is applied during summer, cloud-free months. [46] Corrections to Qs have been suggested by other researchers in the past. Gilman and Garrett [1994], for example, found that daily (instead of monthly) data for the cloudiness in the Mediterranean may cause a reduction of up to 4% (or about 8 W m2) to the solar radiation. This effect is overlooked in our study, because of the monthly mean data used here. However, this reduction is too small to account for the discrepancy between the estimates and the observations. They also showed that Qs may be further decreased by 6 W m2 due to aerosol attenuation. This appears to be a reasonable result within error bounds, but their estimate was tested against ground truth observations from a single station in the middle of Cyprus, away from the coast. [47] In a later study Schiano [1996] analyzed daily Qs data recorded during several cruises in the western Mediterranean and suggested that the clear-sky radiation must be corrected for both the aerosol attenuation and the water vapor content in the atmospheric column. For the aerosol depletion she introduced a weaker transmission coefficient of 0.66, but temporally and spatially uniform, while for the absorption due to water vapor she suggested a step function of the absorption coefficient A, depending on the water vapor density; A = 0.15 when rv  12 gm3. [48] The application of Schiano’s [1996] corrections to the bulk formula using cloudiness and water vapor data from the UWM/COADS resulted in a small reduction of 10 W m2 to the 30 year mean Qs to 207 W m2, but still high compared to the 185 W m2 estimated from the WRDC observations. A comparison of the time series estimated using Schiano’s corrections with the ground truth data shows that these corrections cannot capture either the

Table 3. Results of the Comparison Between the Observed and the Estimated Mean Solar Radiation at All Ground Stations, Before and After the Correctiona

Summer (July) Annual mean Winter (January) a

See text for details.

Original RMS, W m2

Corrected RMS, W m2

66.9 28.5 10.5

25.0 15.5 10.7

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TRAGOU AND LASCARATOS: AEROSOL ROLE ON MEDITERRANEAN SOLAR RADIATION

Figure 14. Original (dashed line) and corrected (thin solid line) estimates of the incoming shortwave radiation at station 164200 (Messina, Italy) between January 1980 and December 1989. The thick solid line is the contemporaneous cloudiness fraction from UWM/COADS; the cloudiness scale is on the right axis.

seasonal or the interannual signal in the observations (Figure 5c).

7. Conclusions [49] There are at least two reasons to focus our attention on the solar heat flux of the Mediterranean Sea: first, because the surface forcing is a major component of the dynamic and thermodynamic forcing of the Mediterranean basin, and, second, because the examination of solar heat flux estimates in this basin allows discussion for the validity of parameterization schemes and data sets used for global estimates. [50] The comparison of the estimated solar heat flux with ground truth observations for the Qs at several coastal meteorological stations showed that the former is systematically overestimated by 25 W m2 for a period of 30 years, and the bulk formula cannot capture either the interannual variability of the observations, or the seasonal signal. During summer the formula overestimates Qs whereas during winter Qs is slightly underestimated. As the cloud reduction formula becomes inapplicable for cloud fraction less than 3 octas (which occurs at summer, cloud-free months) the most likely reason for the large differences during summer appears to be the formula for the clear-sky radiation that does not include the spatial and temporal variations of the attenuation due to aerosols and, possibly, to water vapor absorption. [51] As the available data for the aerosol extinction index are not readily related to the transmission coefficient Tr, we have attempted a correction to the Qs from a calibration of the satellite data. This correction resulted in a reduction to the shortwave radiative flux of about 33 W m2 for a period of 30 years (from January 1964 to December 1993). Moreover, the corrected mean time series at all ground stations follows closely the seasonal and the interannual variations of the observed data, and perhaps gives some credibility to our analysis. The RMS of the difference between the mean estimated and measured Qs values at each station is reduced for the corrected estimates. The remaining differences are probably due to the averaging of the calibration factor fc at all 23 stations and then applying it to the whole Mediterranean, and the usage of one seasonal cycle for the spatially varying aerosol extinction TrsA. We also note that the satellite data are provided only for 2.5 years and for a period that does not overlap with the estimates and observations of Qs. This possibly introduces some error in our correction.

[52] Problems in our analysis may as well come from the fact that we have used coastal instead of marine stations. This may give rise to biases due to different cloud amount especially if a station is close to a steep land topography. Also, some stations are close to urban sites, where the presence of anthropogenic aerosols is stronger. We have no means to account for these effects in this study. However, the fact that the statistics of all stations, including island stations, show the same trend gives some credibility to our analysis. [53] In this analysis we have used monthly mean instead of daily mean cloudiness values for the solar radiation estimates. Daily mean values would have been more appropriate as the cloud reduction formula was originally derived for daily mean values. As Gilman and Garrett [1994] pointed out, usage of daily mean instead of monthly values may cause a reduction of about 4% (or 8 W m2) in the long-term mean insolation. Therefore, part of the discrepancy between the observations and our estimates may be explained by this effect. Still, the difference between the estimates and observations is larger (25 W m2), indicating that the formula is missing some processes. [54] The present analysis has shown that a new parameterization scheme for the clear-sky insolation must be found to include the time and space dependence of the transmission coefficient Tr (at least for areas of high aerosol load). Since the Reed cloud formula includes part of the aerosol reduction, this implies that in order to incorporate some regularly measured quantity in the formula that represents aerosol and possibly water vapor attenuation, a major revision in the formula for the solar radiation is needed. The corrective method applied in this work is a small step toward the improvement of the formula, though it depends on the availability of oceanic in situ measurements of solar radiation for long periods, which are very localized and rather scarce in the world ocean. A systematic calibration of the satellite data to oceanic observations in other areas of the world ocean may provide a useful data set for the correction of the solar radiation bulk formula. [55] Acknowledgments. The WRDC data were downloaded from the World Radiation Data Centre homepage (http://wrdc-mgo.nrel.gov). We thank John Sapper and Larry Stowe of NOAA/NESDIS for making available the satellite data set and for explaining details about the data. Chris Garrett, Simon Josey, and two anonymous reviewers are thanked for comments on an earlier draft of the paper.

TRAGOU AND LASCARATOS: AEROSOL ROLE ON MEDITERRANEAN SOLAR RADIATION

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A. Lascaratos, Department of Applied Physics, University of Athens, University Campus, Bldg PHYS-V, Athens 15784, Greece. (alasc@oc. phys.uoa.gr) E. Tragou, Institute of Oceanography, National Centre for Marine Research, P. O. Box 172, Anavyssos 19013, Greece. ([email protected])