Aerosol optical properties and precipitable water vapor column in the atmosphere of Norway Dennis Muyimbwa,1,2,* Øyvind Frette,1 Jakob J. Stamnes,1 Taddeo Ssenyonga,1,2 Yi-Chun Chen,1 and Børge Hamre1 1

Department of Physics and Technology, University of Bergen, Bergen, Norway 2

Department of Physics, Makerere University, Kampala, Uganda *Corresponding author: [email protected]

Received 27 October 2014; revised 15 January 2015; accepted 19 January 2015; posted 20 January 2015 (Doc. ID 225644); published 20 February 2015

Between February 2012 and April 2014, we measured and analyzed direct solar radiances at a groundbased station in Bergen, Norway. We discovered that the spectral aerosol optical thickness (AOT) and precipitable water vapor column (PWVC) retrieved from these measurements have a seasonal variation with highest values in summer and lowest values in winter. The highest value of the monthly median AOT at 440 nm of about 0.16 was measured in July and the lowest of about 0.04 was measured in December. The highest value of the monthly median PWVC of about 2.0 cm was measured in July and the lowest of about 0.4 cm was measured in December. We derived Ångström exponents that were used to deduce aerosol particle size distributions. We found that coarse-mode aerosol particles dominated most of the time during the measurement period, but fine-mode aerosol particles dominated during the winter seasons. The derived Ångström exponent values suggested that aerosols containing sea salt could have been dominating at this station during the measurement period. © 2015 Optical Society of America OCIS codes: (010.1100) Aerosol detection; (010.1110) Aerosols; (120.5240) Photometry; (120.0280) Remote sensing and sensors; (120.5630) Radiometry; (120.2440) Filters. http://dx.doi.org/10.1364/AO.54.001505

1. Introduction

The atmosphere is composed of molecules of gas as well as small solid and liquid particles suspended in the air called aerosols. When the amount of aerosol particles is sufficiently high, their presence is noticeable as they scatter and absorb sunlight and thereby affect visibility and redden sunrises and sunsets. The distribution and composition of aerosols in the atmosphere vary in space and time and depend on meteorological conditions and aerosol sources (e.g., sea salt, volcanic eruption, bush fires, and anthropogenic emissions) [1]. Atmospheric aerosols affect climate in different ways; for example, directly by scattering sunlight back to space and indirectly by acting as cloud condensation nuclei (CCN). The 1559-128X/15/061505-10$15.00/0 © 2015 Optical Society of America

CCN are the basis for cloud formation, as each cloud droplet requires an aerosol particle to condense upon during the process of cloud formation; otherwise clouds could not form [2]. The properties of clouds as well as the evolution and development of precipitation, therefore, depend mainly on the concentration, size, and composition of atmospheric aerosols that can act as CCN [3,4]. Although natural aerosols are necessary for cloud formation, anthropogenic aerosols and smoke may negatively affect the formation of precipitation and cloud radiative properties [5]. Numerous anthropogenic aerosols and smoke particles share the condensed water during cloud formation, thereby reducing cloud droplet size by about 20%–30% [4,6–8], and yet smaller and polluted cloud droplets are inefficient in producing precipitation [9]. Global climate can be affected through modification by aerosols of the amount of energy reflected by clouds and atmospheric circulation patterns 20 February 2015 / Vol. 54, No. 6 / APPLIED OPTICS

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[10]. It is therefore important to monitor aerosol concentration and composition to assess their influence on global climate and water distribution. We can monitor aerosols by using instruments on satellites and aircrafts or on the ground. The aerosol optical thickness (AOT), which is a measure of the attenuation of solar radiation by a column of atmospheric aerosols, and the precipitable water vapor column (PWVC) are the two most important quantities used to monitor aerosols [11]. Through routine global observations of the AOT and PWVC one can determine aerosol optical properties and assess the aerosol influence on the global radiation budget and climate change [11]. However, the lack of accurate information about the spatial and temporal extent of aerosols has complicated the validation of satellite data [12]. The lack of sufficient aerosol data on a global scale has made it difficult to assess the impact of aerosols on the global climate since high-quality, ground-based measurements with adequate temporal coverage are only available at a few locations. Without validation of satellite measurements and augmentation by ancillary ground-based observations, it is difficult to fully understand aerosols’ influence on climate forcing [13,14]. Substantial uncertainties remain in the assessment of long-term trends of global AOT values and other global properties of aerosols due to lack of observations of relevant parameters, high spatial and temporal variability, and relatively short-term observational records [15]. Aerosol optical properties can be retrieved from ground-based measurements obtained with radiometers or sun photometers if accurate instrument calibrations are performed before measurements are done [16]. Because of the ease with which sun photometers can be deployed in the field, they have been increasingly used to measure atmospheric transmittance in both visible and near infrared parts of the spectrum, and thereby used to retrieve the AOT and the atmospheric column abundance of gases such as ozone and water vapor [17]. The Aerosol Robotic Network (AERONET) is a ground-based global network of sun/sky radiometers that operates at more than 800 sites worldwide [13]. The network’s data sets are independent of satellite retrievals and are used as ground-based validation of satellite data. Myhre et al. [18] found good agreement between AOT values derived from several satellite instruments and AOT values derived from AERONET data at a number of ground-based stations. Several other studies have shown that AERONET data are high quality [13,19,20], but the network has too few stations to give good global aerosol coverage. Spectral AOT values provide aerosol particle size information. A large spectral variation of the AOT, for example, implies domination of small particles. The Ångström exponent, which is commonly used to characterize the wavelength dependence of the AOT, is used as a qualitative indicator of aerosol particle size [21,22]. An Ångström exponent value less than 1 would indicate size distributions dominated 1506

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by coarse-mode aerosols with radii greater than about 0.5 μm, which are typically associated with dust and sea salt aerosols. Ångström exponent values greater than 2 would indicate size distributions dominated by fine-mode aerosols with radii significantly less than 0.5 μm, which are usually associated with urban pollution and biomass burning [22–24]. Because it is easy to determine the Ångström exponent using surface sun photometer measurements or retrievals from satellite measurements, it has been used extensively by many researchers to study aerosol size distributions [13,25–27]. It has been used to characterize maritime aerosols, biomass burning in South America and Africa, and urban aerosols in Asia [28–33]. The main focus of this study was to use direct solar radiance measurements obtained at a ground-based station in Bergen (60°24′N, 05°19′E) to retrieve spectral AOT and PWVC values and to derive Ångtröm exponents from measurements obtained with a CIMEL sun/sky photometer. Although this study is not part of AERONET, we used a CIMEL instrument similar to those AERONET uses. Our direct solar radiance measurements are available for the period between February 2012 and April 2014. Our method is not new, but we believe we have used it for the first time to derive aerosol properties from CIMEL measurements in the Bergen region, where ground-based aerosol data are non-existent. In the future, we plan to compare these results with estimations of aerosol properties based on Mie theory and radiative transfer modeling and with results derived from available satellite measurements. Since the nearest AERONET coastal site at Andenes is about 1600 km north of Bergen, and the nearest inland AERONET site at Birkenes is about 450 km east of Bergen, we intend to use the results from this ground-based investigation for calibration of satellite aerosol measurements. 2. Instrumentation

The CIMEL (CE 317) sun photometer is a portable instrument from CIMEL Electronique, Paris, France. It is similar to the instrument described by Holben et al. [13] except that it is hand-held. It is equipped with up to six interference filters with center wavelengths at 440, 670, 870, 936, 940, and 1020 nm. They are arranged on a wheel that rotates inside the instrument, and a given filter clicks into position before a measurement is made. The instrument has a metal casing to protect it from bad weather and water. It has a built-in battery that can power it for many days without recharging. It is mounted on a tripod and directed by hand toward the sun or sky when making measurements. The data are stored in a built-in memory and can easily be transferred in ASCII format to a computer. The measured data can be used to retrieve the PWVC, the spectral AOT, and other aerosol properties as well as to provide sky radiances and atmospheric correction of satellite data.

3. Methodology

The extraterrestrial solar radiation is attenuated by particles and molecules in the atmosphere through absorption and scattering. If I 0
λ is the radiance associated with solar radiation of wavelength λ at the top of the atmosphere (TOA), then for a singlescattering, plane-parallel atmosphere the attenuated radiance I
λ at the earth’s surface is described by Beer–Lambert–Bouguer’s law I
λ  I 0
λ exp−m
θτ
λ;

(1)

where τ
λ is the atmospheric total optical thickness (TOT) at wavelength λ, and m
θ is the relative optical air mass, which is a function of the solar zenith angle θ (in degrees), parameterized by Kasten [34] and Kasten and Young [35] as m
θ 

1 : cos θ  0.1500
93.885° − θ−1.253

(2)

The Beer–Lambert–Bouguer law in Eq. (1) applies to the spectral radiance with dimension Wm−2 sr−1 nm−1 . A CIMEL measurement covers an angular cone with a solid angle of about 1°. As the solar disk spans an angle of about half a degree, a calibrated CIMEL radiance will represent only one fourth of the averaged solar disk radiance. But in practice AOT are obtained from instrument response ratios, and therefore the choice of calibration units will not be important. If the solar radiance at the surface and at the TOA are known, then the product of the relative optical air mass m
θ and the TOT τ
λ can be determined from Eq. (1). A.

The Langley Method (LM)

The Langley method (LM) is based on the principle of spectral extinction and on the assumption that the Beer–Lambert–Bouguer’s law is valid. This law is applied to the radiance of the direct solar beam measured for each channel of the sun photometer [36,37]. Let V
λ be the measured voltage response of the sun photometer for wavelength λ. The LM is based on applying the Beer–Lambert–Bouguer’s law to obtain V
λ 

V 0
λ exp−m
θτ
λ R2

(3)

where V 0
λ is the projected instrument response voltage at the TOA at wavelength λ. V 0
λ is also known as the calibration coefficient, not based on an exact radiometric calibration, but on the value the instrument would record outside the atmosphere at an earth–sun distance R, which on average is one astronomical unit (1 AU), varying slightly during the year due to the elliptic orbit of the earth around the sun. If the AOT remains constant throughout the measurement process, a plot of ln V
λ against air mass m, gives a straight line whose slope gives the TOT τ
λ for the measurement period. The ordinate

intercept of a least-squares fit through the data provides the required TOA instrument response voltage or the calibration coefficient V 0
λ. Alternatively, if the calibration coefficient V 0
λ of the instrument is already known, the instantaneous TOT τ
λ can be obtained from any individual measurements by inverting Eq. (3). The accuracy with which τ
λ can be retrieved depends on the uncertainty of the V
λ measurements and the accuracy of the V 0
λ value. The TOT τ
λ is a result of attenuation by molecules (Rayleigh scattering), aerosols (Mie scattering), absorption by ozone, water vapor, and other uniformly mixed gases, and each of these components can be calculated separately. The Rayleigh scattering optical thickness τR
λ can be calculated depending on the barometric pressure at the surface as described by Hansen and Travis [38]. In this study we used the Marggraf and Griggs [39] formula τR
λ  0.00877
λ−4.150.2λ 

P ; P0

(4)

where P is the site pressure, P0 is 1013.25 millibar or the pressure at sea-level, and λ is wavelength in μm. The site pressure was obtained from the pressure data recorded at the Department of Geophysics, University of Bergen, Bergen, Norway (http://veret .gfi.uib.no?action=day_query). Besides attenuation by aerosols and molecules, the only other significant contribution to the TOT τ
λ comes from ozone absorption at some wavelengths [40]. The effect of other mixed gases is constant but can be ignored since most sun photometers use bands outside their influence [16]. Ozone has a variable, but small, effect that can be calculated based on tabulated values of the ozone absorption coefficient and knowledge about the ozone column abundance in Dobson units [41]. The ozone optical thickness is obtained by multiplying the ozone column abundance in Dobson units obtained from climatological charts by the ozone specific absorption coefficient for the wavelength considered [42]. Table 1 shows the ozone specific absorption coefficients at various wavelengths as provided by Vigroux [42]. At 440, 670, 870, and 1020 nm light is only significantly attenuated by aerosols, ozone, and molecular scattering. Thus, the AOT τA
λ can be obtained by subtracting the Rayleigh scattering optical thickness τR
λ and

Table 1. Ozone-specific Absorption Coefficients at Different Wavelengths as Provided by Vigroux (1953) [42]

Wavelength (nm) 441 671 872 1030

Ozone-specific Absorption Coefficients (DU−1 cm−1 ) 3.36 × 10−6 4.55 × 10−5 6.17 × 10−7 0.00

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ozone absorption optical thickness τOz
λ from the TOT τ
λ; i.e., τA
λ  τ
λ − τOz
λ − τR
λ:

relative optical water vapor air mass mw is given by Gueymard [48] as (with θ being the solar zenith angle in degrees)

(5)

The ozone column abundances used were the climatological values derived from satellite data provided by the ozone monitoring instrument (OMI). Data are publicly available at NASA’s website (http://gdata1.sci.gsfc.nasa.gov/daac‑bin/G3/gui.cgi? instance_id=omi).

mw 

Modified Langley Method (MLM)

The calibration coefficient V 0
λ for the water vapor absorption channel at 940 nm is derived using a MLM described by Reagan et al. [43]. The method requires the product of the PWVC (denoted by W) and the relative optical air mass m to be accounted for in addition to the TOT due to aerosols, ozone, and molecular scattering [17,44]. The TOT is estimated by interpolation between the two neighboring channels at 870 and 1020 nm. The MLM can be described as [17,44] V
940 nm 

V 0
940 nm exp−mτ − pW q mq ; (6) R2

which, by taking the logarithm on both sides and rearranging the terms, gives  V 0
940 nm − pW q mq ; ln V
940 nm  mτ  ln R2 (7)


where p and q are constants depending on wavelength, the width and shape of the sun photometer filter function, the atmosphere pressure–temperature lapse rate, and the vertical distribution of water vapor [17]. A plot of the left-hand side of Eq. (7) against mq gives a straight line whose ordinate intercept can be used to determine V 0
940 nm, and whose slope gives W. The sun photometer measurements are affected by atmospheric turbulence, which is responsible for signal fluctuations. The MLM requires a stable water column with low water vapor concentration, which can only be found at high altitudes [45,46]. Therefore, we employed a method due to Pérez-Ramírez et al. [47], who proposed a modification of Eq. (7) according to which a relative optical water vapor air mass mw is used to replace m on the right-hand side, after which the new equation is divided through by mqw to give  ln V
940 nm  mτ  mqw

ln



V 0
940 nm R2 mqw

− pW q :

(8)

The constants p and q are listed by Halthore et al. [17], where for the mid-latitudes, p  0.616 and q  0.593 for summer and 0.597 for winter. The 1508

APPLIED OPTICS / Vol. 54, No. 6 / 20 February 2015

cos θ  0.03114θ


92.47° − θ−1.381

:

(9)

The calibration coefficient V 0
940 nm is obtained from the slope of the least squares fit of the plot of the left-hand side of Eq. (8) against 1∕mqw . C.

B.

1 0.10

Ångström exponent

Ångström [21] described a formula to approximate the spectral dependence of the AOT τA  βλ−α

(10)

where the wavelength λ is in μm, α is the Ångström exponent, and β is the Ångström turbidity coefficient, which is equal to the AOT at 1 μm. The sun photometer measurements are carried out at several spectral bands each having a specific center wavelength. The values of the Ångström exponent were computed in the wavelength interval 440– 870 nm by using measured AOT values in the same spectral interval as described by Holben et al. [19]. The spectral calibration coefficients V 0
λ obtained from the LM at different wavelengths were used to retrieve the spectral AOT, except for the 940 nm channel, which was used to retrieve PWVC in centimeters using the MLM method. The aerosol particle size distributions were deduced from the Ångström exponents. The results are analyzed and discussed in the following section. 4. Results and Discussions

We used direct sun measurements made on full, clear-sky days to retrieve the calibration coefficients V 0
λ using the LM and MLM methods given in Eqs. (3) and (8). Figure 1 shows some of the LM calibration results at 440 nm and the MLM results at 940 nm that were obtained on May 27, 2012. The portable CIMEL sun/sky photometers have no temperature stabilization and their radiance measurements vary with the instrument temperature [13,49,50]. The calibration coefficients V 0
λ obtained during the measurement period varied with instrument temperature as shown by the dotted lines in Figs. 2(a)–2(d) for the 440, 670, 870, and 1020 nm channels, respectively. For the 440, 670, 870, and 1020 nm channels, the correlation coefficient R between instrument temperature and calibration coefficient V 0
λ had fairly high values of −0.42, −0.58, −0.59, and −0.43, respectively at 440, 670, 870, and 1020 nm, indicating a significant dependence of the radiance measurements on instrument temperature. If not corrected for, this temperature variation would affect the values of the retrieved AOT and hence the values of the Ångström exponent. For each channel, the calibration coefficient V 0
λ was temperature corrected according to a method

described by von Hoyningen et al. [50]. If V
λ; T is the radiance measured by a CIMEL instrument channel at wavelength λ and temperature T, then the temperature corrected radiance V
λ is given by V
λ  1  T c TV
λ; T;

Fig. 1. Calibration results obtained on May 27, 2012, (a) LM at 440 nm and (b) MLM at 940 nm.

(11)

where T c is the relative voltage change per °C for the channel under consideration. To correct for the temperature dependence of the calibration coefficient V 0
λ for each channel and thus make the corresponding correlation coefficient as small as possible, T c values of 0.44, 0.50, 0.52, and 0.25% per °C were found for the 440, 670, 870, and 1020 nm channels, respectively. The solid lines in Figs. 2(a)–2(d) show the variation of the calibration coefficient V 0
λ with instrument temperature for the 440, 670, 870, and 1020 nm channels, respectively, after temperature correction, indicating that for each channel V 0
λ is much less influenced by changes in instrument temperature than before correction. For the 440, 670, 870, and 1020 nm channels, the correlation coefficient R between instrument temperature and V 0
λ is seen to have small values of 0.005, 0.060, 0.005, and 0.008, respectively. The temperature corrected values of

Fig. 2. Variation of calibration coefficients obtained from the LM method against instrument temperature before and after temperature correction at (a) 440 nm, (b) 670 nm, (c) 870 nm, and (d) 1020 nm. R
x; y is correlation coefficient, before
x and after
y temperature correction. 20 February 2015 / Vol. 54, No. 6 / APPLIED OPTICS

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V 0
λ were then used to retrieve the AOT at 440, 670, 870, and 1020 nm. Because a full, clear-sky day with stable weather conditions is required to acquire calibration coefficients for the CIMEL instrument, we were unable to obtain calibration coefficients for each of the measurement days. Therefore, the average value of the temperature corrected calibration coefficients (Fig. 2, solid line) was used to derive the AOT for days without calibration data. Also, the radiances from the CIMEL instrument were temperature corrected before they were used to retrieve the AOT. The TOT τ
λ was computed by inverting Eq. (3) and the AOT τA
λ was calculated from Eq. (5) by subtracting Rayleigh scattering and ozone absorption optical thicknesses from the TOT τ
λ. Figure 3 shows the variation of daily mean values of the AOT at the four wavelengths during the measurement period. We observed a seasonal variation of the AOT at all wavelengths with highest values during summer

0.4

and lowest values during winter. At all wavelengths the highest AOT values were measured during the months of June and July, and the lowest values were measured during December and January. Figure 4 shows box-and-whisker plots indicating the monthly median values of the AOT at all four wavelengths. In each box of these plots the three horizontal lines at the bottom, inside, and top of the box show, respectively, the median of the lower part (first quartile) of the data, all data (second quartile), and the upper part (third quartile) of the data. The vertical lines outside each box are the whiskers, which show the range of the data. The scattered points above and below the whiskers are outliers. Figure 4 shows that at 440 nm the monthly median AOT values increased gradually from its lowest value of about 0.06 in January (winter) to its highest value of about 0.16 in July (summer), and then started to decrease gradually to its lowest value of about 0.04 in December (winter). At each 0.4

(a) 440 nm

(a) 440 nm

0.3

0.3

0.2

0.2

0.1

0.1

0 0.4

0 0.4

(b) 670 nm

(b) 670 nm

0.3

0.3

0.2

0.2 0.1

0 0.4

(c) 870 nm

AOT

AOT

0.1

0 0.4 (c) 870 nm

0.3

0.3

0.2 0.2 0.1 0.1

0 0.4

(d) 1020 nm

0 0.4 (d) 1020 nm

0.3

0.3

0.2 0.2

0.1

0.1

0 J M M J S N J M M J S N J M M Fig. 3. Daily mean values of the AOT at (a) 440 nm, (b) 670 nm, (c) 870 nm, and (d) 1020 nm during the measurement period. The abscissas are labeled at every second month from January 2012 to May 2014. 1510

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0

J

F M A M J

J A S O N D

Fig. 4. Box-and-whisker plots showing monthly median values of the AOT at (a) 440 nm, (b) 670 nm, (c) 870 nm, and (d) 1020 nm during the measurement period.

3

(a)

Precipitable water vapor column [cm]

2

1

0

440 nm 670 nm 870 nm 1020 nm Linear fit at 440 nm

0.6 0.5

AOT

wavelength, there is a big difference between the lowest and highest median monthly AOT values. An AOT value of less than 0.05 would imply a clear sky with relatively few aerosols and maximum visibility, whereas a value of 1 would imply a hazy sky [51]. The PWVC was retrieved by inverting Eq. (8) for the water vapor absorption channel at 940 nm. The AOT at 940 nm was obtained by interpolation between the AOT values at the neighboring 870 and 1020 nm channels [17]. Figure 5(a) shows the variation of the daily mean values of the PWVC during the measurement period. We observed a seasonal variation of the daily mean values of the PWVC similar to that of the AOT with highest values in summer and lowest values in winter. Figure 5(b) is a box-and-whisker graph showing the variation of the monthly median values of the PWVC during the measurement period. We observed that the monthly median values of the PWVC gradually increased from its lowest value of about 0.45 cm in January to its highest value of about 2.0 cm in July, and then started to decrease gradually to its lowest value of about 0.4 cm in December. We also observed that an increase in the AOT values was accompanied by an increase in the PWVC values during the measurement period, as seen in Fig. 6. The influence is most pronounced with the AOT at 440 nm with a sufficiently high correlation coefficient R of about 0.48 and is least pronounced at 870 nm with a correlation coefficient of about 0.39. AOT values are high at high PWVC values

0.4 0.3 0.2 0.1 0 0

0.5

1

1.5

2

2.5

Precipitable water vapor column [cm] Fig. 6. Scatter plot of AOT versus PWVC during the measurement period. The correlation coefficients R between PWVC and AOT at 440, 670, 870, and 1020 nm were respectively 0.48, 0.43, 0.39, and 0.43.

because aerosol particles tend to grow in sizes due to water coating in humid surroundings. Figure 7 is a box-and-whisker graph showing the variation of monthly median values of the Ångström exponent during the measurement period. The highest values were measured during winter and the lowest values were measured during summer. The highest monthly median values of the Ångström exponent (greater than 1) were measured in January and December (winter), while values smaller than 1 were recorded in other months. This variation in the Ångström exponent suggests a variation of the aerosol particle size distribution during the measurement period with domination of coarse-mode aerosols during summer and fine-mode aerosols during winter [22,24]. Although the AOT values were small for most of the days during winter, the values of the Ångström exponent were greater than 1, suggesting that the atmospheric extinction was due to fine-mode aerosols that were mainly due to urban pollution and biomass burning. If such a situation would occur on a day with a temperature inversion, it could have serious health implications for city residents because polluted air would be trapped close to the surface.

J M M J S N J M M J S N J M M

3

4

(b)

3

2 2 1

1

0

0

J F M A M J

J A S O N D

Fig. 5. (a) Variation of PWVC during the measurement period from January 2012 to April 2014 (The abscissas are labeled at every second month from January 2012 to May 2014). (b) Boxand-whisker plot showing monthly median values of PWVC.

−1 −2

J

F M A M

J

J

A

S O N

D

Fig. 7. Box-and-whisker plot showing monthly median values of the Ångström exponent during the measurement period. 20 February 2015 / Vol. 54, No. 6 / APPLIED OPTICS

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0.4

440 nm 670 nm 870 nm 1020 nm

0.3

0.2

0.1

0 −4

−2

0

2

4

6

8

Fig. 8. Scatter plots of the AOT versus the Ångström exponent during the measurement period.

For most of the measurement days, AOT values at all wavelengths were due to extinction by coarsemode aerosols with Ångström exponent values less than 1, as seen in Fig. 8. Since Bergen is a coastal city and Ångström exponent values less than 1 dominated the measurements, we are led to suggest that aerosols containing sea salt were the dominant type because such values would indicate the presence of coarse-mode aerosols that are mainly based on dust and sea salt [22–24]. Similarly, we suggest that anthropogenic or urban aerosols dominated during winter because the values of the Ångström exponent were greater than 1 and such values are indicative of the presence of fine-mode aerosols, which are mainly anthropogenic aerosols and smoke particles due to biomass burning. However, some of the observed change from fine-mode aerosols during winter to coarse-mode during summer probably could be caused by increased water coating of a distribution of sea-salt-based aerosols. Though the AERONET site at Birkenes is nearest to Bergen, it is an inland site. Therefore, in Fig. 9 we only considered AERONET measurements from Andenes since it is a coastal site. Figure 9 shows the variation of the AOT derived from our measurements in Bergen and the level 2.0 quality assured AOT results from AERONET measurements at Andenes. For Andenes, there were no AERONET data available for 2012 and 2014. In Fig. 9, only days with measurements from both sites were considered. We observed that AOT values from Bergen agree well with those from Andenes at 440 and 670/675 nm, but are slightly higher at 870 nm. This situation could be due to the difference in locations of the two sites, with Bergen at 60°24′N, 05°19′E and Andenes at 69°19′N, 16°7′E as well as the fact that Bergen is a big coastal city about 1600 km south of the remote coastal site of Andenes. PWVC measurements from both sites were available only for days 269 and 270, when the measurements at Andenes were, respectively, about 14% and 3% higher than those in Bergen. 1512

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Fig. 9. Daily mean values of AOT from Bergen and AOT values from AERONET site at Andenes at (a) 440 nm, (b) 670/675 nm (670 nm at Bergen and 675 nm at Andenes), and (c) 870 nm during the year 2013.

5. Conclusions

We have analyzed radiance measurements obtained with a CIMEL instrument at the University of Bergen, Norway. Spectral AOT values at 440, 670, 870, and 1020 nm have been retrieved from the instrument spectral response voltages, and were found to have a seasonal variation. The highest values of the AOT were measured during summer and the lowest values during winter. At 440 nm, the highest median monthly value of about 0.16 was measured in July and the lowest value of about 0.04 was recorded in December. The retrieved PWVC was found to have a seasonal variation with highest values measured during summer and lowest values during winter. The highest median monthly value of about 2.0 cm was measured in July and the lowest value of about 0.4 cm was recorded in December. We found that an increase in the value of the PWVC was accompanied by an increase in the value of the AOT. We expected the size of the coarse-mode aerosol particles to grow with humidity and this growth to be responsible for the observed high AOT values at high PWVC values. Ångström exponents have been found to vary with season with values less than 1 dominating most of the measurement days but with values greater than 1 dominating during winter. Aerosols containing sea salt have been suggested to be the dominant type, except that urban aerosols have been suggested to dominate during the winter season.

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