Dust layer effects on the atmospheric radiative budget and heating rate profiles

Atmospheric Environment 59 (2012) 344e354

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Dust layer effects on the atmospheric radiative budget and heating rate profiles Maria Rita Perrone a, *, A.M. Tafuro a, S. Kinne b a b

CNISM, Department of Physics, Università del Salento, via Arnesano, 73100 Lecce, Italy Max Planck Institute für Meteorologie, Bundesstrasse 53, 20146 Hamburg, Germany

h i g h l i g h t s < Dust aerosol direct radiative effects and heating rates. < Radiative transfer calculations in the solar and terrestrial domain. < Instantaneous and daily average radiative transfer calculations. < Effects of aerosol properties on heating rates and direct radiative effects.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 January 2012 Received in revised form 4 June 2012 Accepted 6 June 2012

The effect of mineral aerosol optical properties and vertical distribution on clear-sky, instantaneous and daily-average aerosol direct radiative effects (DREs) and heating rates (HRs) is analyzed in the solar (S, 0.3e4 mm) and terrestrial (T, 4e80 mm) spectral domain, respectively. The used radiative transfer model is based on lidar, sun-sky photometer, and radiosonde measurements. The study focuses on the Sahara dust outbreak of July 16, 2009 which advected dust particles from north-western Africa over south-eastern Italy. Clear-sky, instantaneous aerosol DREs and HRs undergo large changes within few hours, for the variability of the dust aerosol properties and vertical distribution. The daily-average, clearsky aerosol S-DRE is near
5 Wm
2 and
12 Wm
2 at the top of the atmosphere (ToA) and surface (sfc), respectively. The daily-average aerosol T-DRE offsets the S-DRE by about one third at the ToA and by about one half at the surface. The daily average aerosol HR integrated over the whole aerosol column is 0.5 and
0.3 K day
1 in the S and T domain, respectively. Thus, the all-wave integrated HR is 0.2 K day
1. These results highlight the importance of accounting for the interaction of dust particles with T and S radiation. Sensitivity tests indicate that the uncertainties of the aerosol refractive index, size distribution, and vertical distribution have on average a large impact on aerosol HRs in the S and T domain, respectively. Refractive index and aerosol size distribution uncertainties also have a large impact on Sand T-DREs. The aerosol vertical distribution that has a negligible impact on aerosol S-DREs, is important for aerosol T-DREs. It is also shown that aerosol HRs and DREs in the terrestrial domain are affected by the water vapour vertical distribution. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Aerosol direct radiative effects Aerosol heating rates Desert dust properties Aerosol vertical profiles

1. Introduction The dust role in the Earth’s radiation budget is widely recognized and several experiments have been undertaken to investigate the impact of dust aerosols on the West Africa Monsoon dynamics (e.g. Derimian et al., 2008; Lemaitre et al., 2010) and the Mediterranean radiation budget (e.g. Santese et al., 2010; Bergamo et al., 2008; Gomez-Amo et al., 2011). A comprehensive measurements and modelling radiative closure study of the radiative impacts of

* Corresponding author. Tel.: þ39 0832 297498; fax: þ39 0832 297592. E-mail address: [email protected] (M.R. Perrone). 1352-2310/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2012.06.012

mineral dust has recently been reported by Haywood et al. (2011), where main results on a number of measurement studies performed over land areas of the Sahara Desert are also summarized. The radiative impact of dust particles is rather complex since they are of large size and exert a significant direct radiative effect in both the solar (S) and terrestrial (T) radiation. Solar and terrestrial direct radiadive effects (DREs) by dust particles are generally opposite at the top of the atmosphere (ToA): the presence of the dust generally increases (except above high surface albedo) upward flux densities in the solar and decreases them in the terrestrial spectral domain. Moreover, the absorption and scattering of dust particles in the Tdomain enhance the greenhouse effect and as a consequence, aerosol T-DREs are positive at the surface while S-DREs are

M.R. Perrone et al. / Atmospheric Environment 59 (2012) 344e354

negative. These results highlight the importance of accounting for the interaction of dust particles with T and S radiation, to properly define how they alter the energy budget at the ToA, surface, and within the aerosol column. Modelling aerosol T-DREs contains many uncertainties according to Sokolik and Toon (1999) since, for example, mineral aerosols have complex, highly variable optical properties that, for equal loadings, can cause differences in the surface terrestrial flux density between 7 and 25 Wm
2. GuerreroRascado et al. (2009) demonstrated the atmospheric stabilization effect by dust solar absorption and heating for an extreme Saharan dust event detected over the southern Iberian Peninsula. The sensitivity of the radiative flux density, forcing, and heating rate (HR) to the aerosol vertical profile has recently been investigated by Guan et al. (2010) in the solar domain. Their results indicate that the vertical shape of the aerosol extinction profile has very little impact on clear-sky S-DREs but it is important for HR profiles. In this paper the sensitivity of solar and terrestrial DREs and HR profiles to the optical properties and vertical distribution of dust particles is studied. To this end, the dust outbreak which has affected south eastern Italy in July 16, 2009 has been analyzed as case study. Aerosol optical and microphysical properties from ground-based sunesky photometer measurements and aerosol vertical profiles from lidar measurements, performed within AERONET (Holben et al., 1998) and EARLINET (e.g. Matthias et al., 2004) respectively, are used to calculate clear-sky, instantaneous, and daily average aerosol DREs and HR profiles in the solar (0.3e4 mm) and terrestrial (4e80 mm) domain by a two stream radiative transfer model. A brief overview of the used radiative transfer model is given in Section 3. Sections 4 and 5 focus on the characterization of the dust event and model results, respectively. Sensitivity tests are presented and discussed in Section 6. 2. Site and instrumentation Atmospheric aerosol measurements have been performed at the Physics Department of the University of Salento, Lecce (40 N; 18 E) in southeastern Italy. Due to its location, the monitoring site of this study can be affected by the long range transport from three main aerosol sources: (1) the Sahara desert, which can be responsible for the increase of coarse mode particles due to crustal material, (2) northern and north-eastern Europe, which can increase the concentration of fine particles of anthropogenic origin, and (3) the Atlantic and Mediterranean Sea which may cause the advection of marine aerosols. Lidar measurements are regularly performed at the Physics Department with a Raman lidar which is operative within EARLINET since May 2000. The lidar nowadays employs an f/4 Newton telescope with a 30-cm-diameter mirror and a frequency-tripled Nd-YAG laser (355 nm) that delivers pulses of w350 mJ of energy at a repetition rate of 30 Hz. Details on experimental apparatus and data analysis are reported in De Tomasi and Perrone (2003) and De Tomasi et al. (2006). A CIMEL sunphotometer operating within AERONET is also operative at the lidar site since 2003. The photometer measures direct sun radiance in eight spectral channels between 340 and 1020 nm (340, 380, 440, 500, 675, 870, 940, and 1020 nm). Sky measurements are performed at 440, 675, 870, and 1020 nm through a wide range of scattering angles from the Sun. The new AERONET retrieval scheme (Dubovik et al., 2006) considers aerosol particles made by a mixture of polydisperse, randomly oriented, homogeneous spheroids with a fixed distribution of aspect ratios and accordingly, it provides the fraction (in percentage) of spherical particles (sphericity parameter). AERONET products (http:// aeronet.gsfc.nasa.gov) include Aerosol Optical Depths (AOD), real (n) and imaginary (k) refractive indices and size distributions in 22

345

logarithmically equidistant bins in the radius range 0.05e15 mm. A discussion on the accuracy of the AERONET products is reported in Dubovik et al. (2000, 2002). The AERONET inversion algorithm constraints particles with radius r larger than 15 mm to be absent. As a consequence of this assumption, the fine mode size distribution can be overestimated if large particles (r > 15 mm) contribute to the aerosol column burden, according to Kleidman et al. (2005). More specifically, they have shown that when the ratio of the AOD by fine mode particles to the whole AOD, denoted fine-mode-fraction, takes values smaller than 0.6, the Dubovik inversion of sky radiances may overestimate the fine-mode-fraction by 0.1e0.2 relative to the O’Neill method of inverting AERONET AOD spectra. Finally, a net radiometer (Q-7.1 by CAMPBELL SCIENTIFIC INC, USA) is used to monitor broadband all-wave fluxes in the 0.25e60 mm spectral range, with two hemispheric (up and down) sensors, which do not allow separating solar and terrestrial component. The net radiometer which regularly operates at the ISAC-CNR Department of Lecce (www.basesperimentale.le.isac.cnr. it) is characterized by 6% accuracy and is located few hundred meters away from the lidar site. 3. The two-stream radiative transfer model Radiation Transfer Models (RTMs) are used to compute aerosol radiative effects. A detailed description of the two-stream RTM used in this study is given in Tafuro et al. (2007) and Perrone and Bergamo (2011). Twenty homogeneous plane-parallel atmospheric layers are used in the model to account for the changes with altitude of atmospheric parameters and components, and radiative flux densities are determined in the solar (0.3e4 mm) and terrestrial (4e80 mm) domain. Eight solar and twelve terrestrial sub-bands are considered to properly account for the spectral dependence of atmospheric particle properties. Input data include instantaneous size distributions and real (n) and imaginary (k) refractive indices from AERONET sunphotometer measurements performed at Lecce. n and k values are retrieved at 0.44, 0.675, 0.87, and 1.02 mm, respectively. For the near-infrared region of the solar spectrum, the AERONET refractive indices extracted for the wavelength at 1.02 mm are applied. n and k values for mineral aerosol from the Global Aerosol Data Set (GADS, Koepke et al., 1997) are used in the far infrared spectral region. Mie calculations (assuming a spherical particle shape) are applied to translate the data on size, concentration, and refractive indices into AODs, single scattering albedo (SSA), and asymmetry-factor (g). Notice that AOD, SSA, and g values from the Mie code were in accordance with corresponding AERONET optical parameters at least within AERONET accuracy levels. Surface albedo values are based on the ones assumed for Lecce in the AERONET inversion code at the wavelengths corresponding to sky radiance measurements. A surface emissivity of 0.96 is assumed in the far-infrared. Lidar measurements are used to characterize the aerosol vertical distribution. Data from a ground-based meteorological station (Laboratorio di Micrometeorologia, Dipartimento di Scienza dei Materiali) operating few hundred meters from the lidar site are used to define density, pressure, temperature, and water vapour at the surface. Then, radiosonde measurements (see also http://esrl.noaa.gov/raobs/) at the meteorological station of Brindisi that is 40 km north-west of Lecce are used to define vertical profiles of density, pressure, temperature, and water vapour from 1 up to 20 km altitude. Above 20 km of altitude, vertical profiles of density, pressure, temperature, and water vapour are extended with corresponding mid-latitudes standard atmosphere data provided by the Air Force Geophysics Laboratory (AFGL) for spring-summer months (Anderson et al., 1986). Vertical profiles of oxygen, ozone, and wellmixed trace gases (N2O, CO2, CO and CH4) are prescribed by the AFGL standard atmosphere for mid-latitude (30 e60 N) spring-

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AOD (440 nm)

0.25

1.00 0.90

0.20 AOD η

0.80

0.15 0.70 0.10

0.60

0.05

0.50 14

4. Characterization of the July 16, 2009 dust-event effects Lidar systems represent the best devices to monitor the time evolution of the aerosol vertical distribution and infer the advection of long-range transported air masses. Fig. 1aef (lines) shows the aerosol backscatter coefficient profiles retrieved from polarization sensitive lidar measurements at 355 nm, performed in July 15 and 16, 2009 at different day hours (UTC). Dotted lines represent volume depolarization ratio vertical profiles. An altitude independent lidar ratio Sa ¼ 60 sr is used to retrieve the b(z) profiles (Pavese et al., 2009; De Tomasi et al., 2006). The statistical uncertainties of b(z) are calculated from the error propagation law by assuming a Poisson noise on lidar signals and are smaller than few percents. d(z) uncertainties are lower than 10%. The b(z) profile in Fig. 1a reveals that aerosol particles are located up to w2.5 km from ground in July 15. Then, an additional aerosol layer with d(z) values larger than 0.02, is revealed by lidar measurements since the early morning of July 16 (Fig. 1bef). The high variability of the aerosol vertical distribution (Fig. 1), which is usually observed during dust outbreaks, and d(z) profiles indicate that non spherical particles as those due to desert dust, contribute to the aerosol load above 2 km from ground. This comment is supported by sun/sky photometer measurements. Fig. 2 shows the evolution of the AOD (open dots) and the fine-mode fraction (grey triangles) at 440 nm from 14 July to 17 July 2009. The AOD increases from 15 up to 16 July, in accordance with lidar measurements. In addition, fine-mode fraction (h) values that span the 0.81e0.91 range in July 15, vary within the 0.56e0.57 range in July 16, for the larger contribution of coarse mode particles as those due to desert dust.

15

16 July, 2009

17

18

Fig. 2. Time evolution of the AOD (open dots) and the fine-mode fraction (grey triangles) at 0.44 mm from July 14 up to July 17, 2009.

5-day analytical back trajectories of July 16, 2009 at 12:00 UTC from the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (http://ready.arl.noaa.gov/HYSPLIT.php) show that the back trajectory at the 3-km level travels over southwestern Algeria before reaching the monitoring site and supports the advection of dust particles above 2.5 km from the ground level. The July 16, 2009 dust event has also been predicted by forecast models (e.g. www.nrlmry.navy.mil/aerosol/ and www.bsc.es/ projects/earthscience/DREAM). More specifically, the DREAM model predicts that the dust event affects the west and central Mediterranean and that AODs at 0.55 mm span the 0.14e0.4 range over south-eastern Italy. The dust concentration profile predicted by the DREAM model for July 16 (12 UTC) at the lidar site shows that dust particles are located from 2 up to 6 km, in accordance with polarization sensitive lidar measurements. 5. Dust aerosol properties and radiative transfer model results for July 16 Fig. 3 shows the columnar volume size distributions retrieved in July 16, from AERONET measurements at different day hours. The volume size distribution retrieved in July 15 at 16:51 UTC is also shown in Fig. 3 (grey dashed line) to highlight the larger δ

0.00 0.04 0.08 0.00 0.04 0.08 0.00 0.04 0.08 0.00 0.04 0.08 0.00 0.04 0.08 0.00 0.04 0.08 7 15/07/2009 12:36 - 13:47 UTC

6

16/07/2009 07:27- 08:27 UTC

a

16/07/2009 09:28 -10:29 UTC

16/07/2009 11:31 - 12:33 UTC

c

b

16/07/2009 13:35 - 14:38 UTC

16/07/2009 16:43-17:37 UTC

e

d

f

Altitude (km)

5 4 3 2 1 0 0

2

4 0

2

4 0

2

η (440 nm)

summer. Trace gas concentrations in conjunction with predefined absorption coefficients are used to compute gas absorption. Radiative transfer calculations for daily average aerosol DREs assume that water vapour and temperature profiles remain unchanged. Then, in order to account for the diurnal Sun-elevation changes, solar radiative transfer simulations are performed at five different Sunelevations (Bergamo et al., 2008). Site-latitude and corresponding average Sun height above the horizon are used to determine fractional day-periods. All available data are then properly weighted to get daily averaged solar flux densities.

4 0

2

Backscatter Coefficient (Mm

4 0

2

4 0

2

4

-1

-1 sr )

Fig. 1. aef Backscatter coefficient (lines) and depolarization ratio (dots) vertical profiles at different hours of (a) 15 and (bef) 16 July, 2009.

M.R. Perrone et al. / Atmospheric Environment 59 (2012) 344e354

2

0.04

dV/dln r (μm /μm )

0.05

3

0.06

16/07/2009 15/07/2009 04:59 16:51 05:26 06:21 06:53 16:50

0.03 0.02 0.01 0.00 5 6 7

2

3

4 5 6 7

0.1

2

3

4 5 6 7

1 Radius (μm)

10

Fig. 3. Columnar volume size distributions from AERONET sun/sky photometer measurements at different day hours of July 16, 2009. The volume size distribution retrieved in July 15, 2009 at 16:51 UTC is also plotted for comparison.

contribution of coarse mode particles in July 16. The sphericity parameter (percentage fraction of spherical particles) that is 99 in July 15 spans the 0e3 range in July 16. n and k values from AERONET measurements at different day hours of July 16 are plotted (symbols) versus wavelength in Fig. 4a and b, respectively. Size distributions, sphericity parameters, and refractive indices of this study are within the variability range found over south-eastern Italy during dust events (e.g. Perrone and Bergamo, 2011). Solid and dotted lines in Fig. 4a and b represent n and k values for mineral aerosol from Sokolik and Toon (1999) and GADS, respectively. GADS

3.0

Sokolik and Toon GADS 04:59 UTC 05:26 UTC 06:21 UTC 06:53 UTC 16:50 UTC

n

2.5

2.0

347

displays on average larger n and k values for mineral aerosol. In particular, the quite large values of k in the solar domain are expected to be responsible for larger atmospheric forcing and hence, HRs. n AERONET values retrieved at selected wavelengths (0.44, 0.675, 0.87, and 1.020 mm) are in good accordance with corresponding Sokolik and Toon (1999) and GADS values within the limit of their uncertainty. k AERONET values at 06:21 and 06:53 UTC are in reasonable accordance with corresponding values from Sokolik and Toon (1999). k AERONET values at 16:50 UTC are in satisfactory accordance with corresponding GADS values. The water vapour mixing ratio (WVMR) profile from radiosounding (RS) measurements at Brindisi (Italy) at 11:00 UTC is plotted in Fig. 5 (solid line), in addition to the mean WVMR profile by AFGL for mid-latitude spring-summer months (grey line), and the b(z) profile (dotted line) retrieved from lidar measurements performed from 11:31 up to 12:33 UTC (Fig. 1c). The AFGL-WVMR profile decreases smoothly with altitude. Conversely, RS-WVMR and b(z) profiles have a similar trend and their maxima almost appear at the same heights. In particular, Fig. 5 reveals enhanced levels of water vapour within the dust layer above 2 km from ground, even if the atmosphere usually is considered to be dry during dust events (Kim et al., 2004). A strong correlation between the spatial and temporal evolution of b(z) and WVMR is on average found at the lidar site (De Tomasi and Perrone, 2003). The high WVMRs near the surface are due to a humid planetary boundary layer. 5.1. Solar and all-wave model flux densities at ground and intercomparisons Fig. 6a shows the scatterplot of model instantaneous solar flux densities at the surface versus the corresponding instantaneous solar flux densities at the surface by AERONET. The radiative -1

-1

β(z) (Mm sr ) 0.0 7

2.0

3.0

Radio Sound 11 UTC AFGL β (z)

1.5

6

a

1.0

1.0 4 5 6 7

2

1 1

3

4 5 6 7

2

3

4 5 6 7

10 Wavelenght (μm)

Sokolik and Toon GADS 04:59 UTC 05:26 UTC 06:21 UTC 06:53 UTC 16:50 UTC

k

0.1

5 Altitude (km)

3

4 3 2

0.01

1

b

0.001 3

4 5 6 7

2

1

3

4 5 6 7

2

3

4 5 6 7

10 Wavelenght (μm)

Fig. 4. (a) n and (b) k values from AERONET sun/sky photometer measurements performed in July 16, 2009 at different day hours (symbols). Solid and dotted lines represent n and k values from Sokolik and Toon (1999) and the Global Aerosol Data Set (GADS, Koepke et al., 1997), respectively.

0 0

4

8

12

16

WVMR (g/kg) Fig. 5. Water vapour mixing ratio (WVMR) profile from radio sounding measurements performed at Brindisi (Italy) in July 16, 2009 at 11:00 UTC (solid line). The grey line represents the WVMR profile from AFGL for mid-latitude (30 e60 N) spring-summer months. The dotted line represents the b(z) profile of Fig. 1d.

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M.R. Perrone et al. / Atmospheric Environment 59 (2012) 344e354

(0.995  0.003) of the least-square fit reveal (Fig. 6a). We believe that these results in addition to the ones reported in Perrone and Bergamo (2011) support the accuracy of our model in the solar domain. Fig. 6b shows the scatterplot of model all-wave net flux densities versus all-wave net flux density measurements from the Q-7.1 net radiation transducer. Terrestrial flux densities from the model are given in brackets to highlight their role with respect to corresponding all-wave flux densities. The ratio of the model terrestrial flux density to the all-wave flux density is equal to
1,
0.6,
0.3,
0.3, and
4 at 04:59, 05:26, 06:21, 06:53, and 16:50 UTC, respectively. Fig. 6b shows a relatively good agreement (r ¼ 0.98) between model and measured all-wave net flux densities, with the exception of the sunset value (D). The two stream method that is less accurate at sunset is likely responsible for this last result. The uncertainties of all-wave flux density measurements which generally increase with q (Derimian et al., 2008) may also contribute. We believe that Fig. 6 plots support the appropriateness of the RTM in the (S þ T) domain.

y= (0.995±0.003) x r= 1

2

Model S-flux density (W/m )

500 400 300 200 100

a

0 0

100

200

300

400

500 2

2

Model (S+T)-flux density (W/m )

AERONET S-flux density (W/m )

400

2

(- 96 W/m ) y= (1.08±0.08) x r=0.98

300

5.2. Reference simulation: aerosol DREs 2

(- 88 W/m )

200 2

(- 82 W/m )

100

2

(- 81 W/m )

b

2

0

(- 106 W/m )

0

100

200

300

400 2

Measured (S+T)-flux density (W/m ) Fig. 6. (a) Instantaneous S-flux densities at the surface from the two-stream RTM versus corresponding instantaneous S-flux densities from AERONET and (b) model allwave net flux densities at the surface versus all-wave net flux density measurements from the Q-7.1 net radiation transducer. Terrestrial flux densities from the model are given in brackets. Time of the day (solar zenith angle) of selected data points are: (B) 04:59 UTC (75.3 ), (,) 05:26 UTC (70.3 ), (>) 06:21 UTC (59.8 ), (C) 06:53 UTC (53.8 ), and (6) 16:50 UTC (75.9 ), respectively.

transfer model used within AERONET to estimate broadband radiative flux densities and aerosol DREs is described in Garcia et al. (2008), where validation tests by ground-based broadband measurements are also reported. The AERONET broadband flux densities are calculated by spectral integration in the range from w0.3e2.8 mm using more than 200 size sub-intervals. Thus, the AERONET broadband-radiation calculation is focused on accurate accounting for the spectral dependence of atmospheric gases, aerosol optical properties and surface albedo used as input. AERONET solar flux densities at the surface are in good accordance with our model results as correlation coefficient (r ¼ 1) and slope

Clear-sky, instantaneous, aerosol S- and T-DREs at the top of the atmosphere (ToA) and surface (sfc) calculated at different day hours (solar zenith angles) are given in Table 1. Solar zenith angles, AOD, SSA, and g model data at 0.55 mm, solar atmospheric forcings (AFs) and solar ToA and surface aerosol forcing efficiencies (AFEs) are also reported in Table 1. Solar zenith angles (q) are 75.3 , 70.3 , 59.8 , 53.8 , and 75.9 at 04:59, 05:26, 06:21, 06:53, and 16:50 UTC, respectively. The S-AF is defined as the difference between the aerosol S-DRE at ToA and surface and it is an indicator of aerosol direct effects on the radiation transfer within the atmosphere. The AFE is defined as the radiative effect per unit aerosol optical depth and it is dependent on aerosol size and composition, and on solar zenith angle (as well as the aerosol DRE). S-AFEs are calculated with respect to the AOD at 0.55 mm. AODs of this study (Table 1) are representative over southeastern Italy of typical medium-weak dust intrusion events, according to Perrone and Bergamo (2011). Instantaneous S-DREs are negative at the ToA and surface since aerosol particles determine a planetary and surface cooling. This occurs since aerosols not only scatter but, also absorb solar radiation. In fact, the S-AF increases with the time of the day in July 16 (Table 1), for the increase of the contribution of absorbing particles as k (Fig. 4b, symbols) and SSA values reveal. The S-AF represents the amount of power density trapped within the atmosphere for the presence of absorbing particles. It induces heating effects and hence, a stabilizing effect on the atmospheric stratification. Instantaneous S-AFEs at the ToA (S-AFE ToA) and surface (S-AFEsfc) are in accordance with previous studies (e.g. Di Biagio et al., 2009; Di Biagio et al., 2010; Perrone and Bergamo, 2011; Gomez-Amo et al., 2011). More specifically, Di Biagio et al. (2009) found for desert dust particles that S-AFEsfc values varied from
200 up to
80 Wm
2 when solar zenith angles varied from 60 to 75 . In addition, experimental-

Table 1 Solar zenith angle (q ( )), aerosol optical depth (AOD), single scattering albedo (SSA), and asymmetry parameter (g) at 0.55 mm. Aerosol DRE at the top of atmosphere (DREToA) and at the surface (DREsfc) in the solar (S) and terrestrial (T) spectral range. S atmospheric forcing (S-AF), and S aerosol forcing efficiency at the top of atmosphere (S-AFEToA) and at the surface (S-AFEsfc).

q( )

AOD

SSA

g

S-DREToA (Wm
2)

T-DREToA (Wm
2)

S-DREsfc (Wm
2)

T-DREsfc (Wm
2)

S-AF (Wm
2)

S-AFEToA (Wm
2)

S-AFEsfc (Wm
2)

75.3 70.3 59.8 53.8 75.9

0.19 0.19 0.20 0.19 0.19

0.96 0.96 0.93 0.94 0.89

0.66 0.65 0.65 0.65 0.67


16.7
17.4
15.5
14.0
11.7

1.2 1.3 1.6 1.7 1.8


19.0
21.4
26.6
24.2
22.6

5.3 5.3 6.0 6.0 7.2

2.3 4.0 11 10 11


88
92
77
74
62


100
113
133
127
119

M.R. Perrone et al. / Atmospheric Environment 59 (2012) 344e354

based estimates of S-AFEs for desert-dust, have revealed that the SAFEToA and the S-AFEsfc were (
64  21) Wm
2 and (
136  12) Wm
2, respectively for solar zenith angles within the 35 e45 range (Di Biagio et al., 2010). Aerosol scattering and absorption of solar radiation reduce the flux density reaching the surface and even a small aerosol backscatter can cause a negative aerosol DRE at the surface. Conversely, the terrestrial DRE at the surface is always positive. This is caused by the additional downward flux density from aerosol layers. At the ToA, the presence of dust generally increases (except above high surface albedo) solar upward flux densities and decreases the outgoing terrestrial radiation (except for strong inversion cases when the temperature of the aerosol is higher than the surface temperature). Thus, S and T aerosol DREs are generally opposite, since the aerosol absorption and scattering in the terrestrial domain enhance the greenhouse effect (Dufresne et al., 2002). Instantaneous T-DREs span the 5.3e7.2 Wm
2 range at the surface (Table 1) offsetting 22e32% of the (negative) S-DREsfc. Instantaneous T-DREToA values vary within the 1.2e1.8 Wm
2 range offsetting 7e15% of the (negative) S-DREToA. Aerosol T-DREs of this study are in reasonable accordance with the ones reported by Markowicz et al. (2003a). They found that T-DREs are often a few Wm
2 and can reach almost 10 Wm
2 at the surface for large aerosol loadings. Daily-average DREToA and DREsfc values in the S and T domain, respectively and solar AFs and AFEs are given in Table 2 in addition to daily-average AOD, SSA, and g model data at 0.55 mm. The dailyaverage T-DRE offsets 34% and 51% of the S-DREToA and S-DREsfc, respectively. This is because the terrestrial heating occurs throughout the day, but solar cooling is only during the sunlit portion of the day (Kim et al., 2004). These results highlight the importance during dust events, to understand how S- and T-DREs act together and alter the energy budget at the ToA and surface (Huang et al., 2009). Daily-average S-AFEs of this study are in satisfactory accordance with the ones reported by Li et al. (2003), Markowicz et al. (2003b), and Zhou et al. (2005) for dust particles. 5.3. Reference simulation: HR vertical profiles The heating rate is determined by the vertical divergence of the net radiative flux density (e.g. Guerrero-Rascado et al., 2009):

vT=vt ¼




  1= rCp ðvF=vzÞ

(1)

where T is the temperature, r is the air density, Cp is the specific heat of air at constant pressure, and vF/vz is the divergence of the net radiative flux density with height. The aerosol heating rate is the difference in heating rates between an aerosol-laden and an aerosol-free atmosphere. Instantaneous S (dotted line), T (grey line), and all-wave (S þ T) aerosol heating rates (black line) calculated at 06:53 and 16:50 UTC

349

are plotted in Fig. 7a and b, respectively as an example. The AOD fraction profiles calculated from the b(z) profiles plotted in Fig. 1b and f are used for the RTM simulation at 06:53 and 16:50 UTC, respectively. Lidar measurements are available since 07:27 UTC in July 16. As a consequence, the b(z) profile plotted in Fig. 1b is used for the RTM simulation at 06:53 UTC. Aerosol S- and T-HRs vary with altitude as the aerosol backscatter coefficient (Fig. 1b and f). Hence, dust particles are mostly responsible at 06:53 UTC for S-HRs y 0.18 K day
1 and T-HRs y
0.05 K day
1 between 3 and 4.5 km from ground (Fig. 7a). Solar (terrestrial) HRs decrease (increase) below the dust layer for the decrease of both the incoming solar flux density and the aerosol load and again increase (decrease) up to w0.4 K day
1 (w0.15 K day
1) at altitudes close to ground for the larger content of aerosol particles. The dust layer moves downward with the time of the day (Fig. 1) as it generally occurs during dust outbreaks (e.g. Pavese et al., 2009; Perrone and Bergamo, 2011). As a consequence Fig. 7b reveals that the S-HR increases up to w0.3 K day
1 at 2.5 km from ground at 16:50 UTC. The T-HR is w0.1 K day
1 at 2.5 km from ground at 16:50 UTC. Aerosol S-HRs of this study are in accordance with the ones reported by Guan et al. (2010) and Carlson and Benjamin (1980). Satheesh et al. (2002) evidenced a dust-related heating rate between 0.4 and 1.2 K day
1 over northern Africa (10e20 N; 20e30 E) and southern Africa (10e20 S; 20e30 E) using direct observations of solar radiation in the solar part of the spectrum. Aerosol T-HRs show terrestrial cooling up to
0.1 K day
1 within the dust layer and up to
0.2 K day
1 in the lowermost aerosol layer in accordance to Lubin and Satheesh, 2002. Higher (smaller) aerosol S (T) HRs are found in higher-AOD and more absorbing dust layers (e.g. Lubin and Satheesh, 2002; Kim et al., 2004; Guerrero-Rascado et al., 2009; Huang et al., 2009; Guan et al., 2010; Lemaitre et al., 2010). Instantaneous aerosol S-HRs are higher than terrestrial cooling rates through the aerosol column and as a consequence all-wave (S þ T) HRs are positive (heating) at 06:53 and 16:50 UTC. Daily-average S, T, and (S þ T) heating rates by aerosols are plotted in Fig. 7c. Aerosol S, T, and all-wave HRs integrated over the whole aerosol layer (HRint) are reported in Table 3. The daily average (negative) aerosol T-HRint offsets the (positive) S-HRint of 60%. 6. Sensitivity tests We conduct in this section a series of sensitivity studies to evaluate the potential sources of uncertainties to daily-average aerosol DREs and HR profiles. More specifically, we modify the refractive index (n and k) values (A- and B-test), the aerosol size distribution (C-test), the aerosol vertical distribution (D-test), the vertical distribution of fine and coarse mode particles (E-test), and the water vapour vertical distribution (F-test). The sensitivity of the results to the tested parameter is quantified in terms of difference values (DV) and percentage differences (DV%):

Table 2 Daily-average aerosol optical depth (AOD), single scattering albedo (SSA), and asymmetry parameter (g) at 0.55 mm. Aerosol DRE at the top of atmosphere (DREToA) and at the surface (DREsfc) in the solar (S) and terrestrial (T) spectral range, S atmospheric forcing (S-AF), S aerosol forcing efficiency at the top of atmosphere (S-AFEToA) and at the surface (S-AFEsfc) for the reference (ref) simulation and the sensitivity test to refractive index (n and k) values (A- and B-test), aerosol size distribution (C-test), aerosol vertical distribution (D-test), vertical distribution of fine and coarse mode particles (E-test), and water vapour vertical distribution (F-test). Test

AOD

SSA

g

S-DREToA (Wm
2)

T-DREToA (Wm
2)

S-DREsfc (Wm
2)

T-DREsfc (Wm
2)

S-AF (Wm
2)

S-AFEToA (Wm
2)

S-AFEsfc (Wm
2)

ref A B C D E F

0.19 0.21 0.19 0.19 0.19 0.19 0.19

0.94 0.90 0.93 0.89 0.94 0.94 0.94

0.66 0.63 0.66 0.79 0.66 0.66 0.66


5.3
5.0
5.0
1.6
5.3
5.4
5.3

1.8 1.8 1.8 4.5 1.4 3.1 1.3


11.8
15.6
11.2
14.3
11.9
11.9
11.8

6.0 6.0 6.6 13.3 6.2 5.0 4.9

6.5 11 6.2 13 6.6 6.5 6.5


28
24
26
8.4
28
28
28.


62
74
59
75
62
62
62

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7

06:53 UTC S T (S+T)

6 5 Altitude (km)

b

a

c

16:50 UTC S T (S+T)

16/07/2009 S T (S+T)

4 3 2 1 0 -0.2

0.0

0.2

0.4

-0.2

0.0

0.2

0.4

-0.2

0.0

0.2

0.4

Aerosol Heating Rate (K/day) Fig. 7. Instantaneous aerosol S, T, and (S þ T) heating rate at (a) 06:53 UTC and (b) 16:50 UTC of July 16, 2009. (c) Daily average aerosol S, T, and (S þ T) heating rate of July 16, 2009.

DV% ¼



Vref
V

(2)  . Vref *100

(3)

where Vref and V represent values of reference and the sensitivity test simulation, respectively. 6.1. Sensitivity tests to refractive index values (A- and B-test) According to Dubovik et al. (2000, 2002), the expected accuracy of the real part of the refractive index is of 0.04 for AOD (0.44 mm)  0.5 and of 0.05 for AOD (0.44 mm)  0.2. k values are instead retrieved with errors of the order of 30e50% for AOD (0.44 mm)  0.5 and of the order of 80e100% for AOD (0.44 mm)  0.2. To test the sensitivity of our model results to n and k uncertainties, we have replaced AERONET refractive index values at 0.44, 0.675, 0.87, and 1.018 mm, respectively with the corresponding ones from GADS (A-test). The differences between n- and k-AERONET values and corresponding GADS values for mineral aerosol are within the AERONET expected accuracy, even if n- and mainly k-GADS values are on average larger than corresponding AERONET values. Main results of this and all following sensitivity tests are summarized in Tables 2 and 3, and Figs. 8, 11 and 12. When solar n- and k-GADS values are used in place of AERONET data, the AOD increases by w10% and both SSA and g decrease by w4% at Table 3 Daily average solar (S), terrestrial (T), and all-wave (all-W) HRs integrated over the whole aerosol layer for the reference (ref) simulation and the sensitivity test to refractive index (n and k) values (A- and B-test), aerosol size distribution (C-test), aerosol vertical distribution (D-test), vertical distribution of fine and coarse mode particles (E-test), and water vapour vertical distribution (F-test). Test

S-HRint (K day
1)

T-HRint (K day
1)

all-W-HRint (K day
1)

Ref A B C D E F

0.50 0.83 0.48 0.99 0.49 0.62 0.49


0.30
0.30
0.34
0.63
0.36
0.12
0.28

0.20 0.53 0.14 0.36 0.13 0.50 0.21

0.55 mm. The percentage differences of the daily-average S-DREsfc and S-AF are
32% and
69%, respectively. The percentage difference of the daily-average S-DREToA is of w6%. The larger GADS values of k for mineral aerosol in the S domain are mainly responsible for the smaller S-DREsfc, the larger S-DREToA, the larger atmospheric heating (Table 2), and the larger S-HRs. The aerosol SHR differences increase up to
0.07 K day
1 and
0.17 K day
1 within the dust layer and at the surface respectively (Fig. 8a, dotted lines) and the percentage differences of the aerosol S-HRint and allwave-HRint are
66% and
165%, respectively (Table 3). With the B-sensitivity test, we have replaced S and T n- and kvalues of the reference simulation with the corresponding values from Sokolik and Toon (1999) for desert dust, which are plotted in Fig. 4 (solid line). With the now smaller solar absorption, the AF and

7

a

b

S

6

(S+T) ref A B C

ref A B C

5 Altitude (km)

DV ¼ Vref
V

T ref A B C

4 3 2 1 0 -0.2

0.0

0.2

0.4

-0.2

0.0

0.2

0.4

Aerosol Heating Rate (K/day) Fig. 8. (a) Daily-average aerosol S- and T-HRs of the reference simulation (solid lines) and test A (dotted lines), B (dashed lines), and C (dot-dash lines). (b) Daily-averaged aerosol all-wave heating rates of the reference simulation (solid lines) and test A (dotted lines), B (dashed lines), and C (dot-dash lines).

M.R. Perrone et al. / Atmospheric Environment 59 (2012) 344e354

6.2. Aerosol size distribution sensitivity test (C-test) Analyses by Dubovik et al. (2000, 2002) have shown that AERONET aerosol volume size distributions (dV(r)/dln r values) are retrieved with accuracy levels of the order of 15e35% for 0.1mm  r  7 mm. However, the errors in estimating very small values of dV(r)/dln r (for example, the values on the tails of the size distribution at r < 0.1 mm and r > 7 mm) can be much higher. In addition, the AERONET inversion algorithm can overestimate the fine mode size distribution during dust outbreaks (Kleidman et al., 2005). Then, we have assumed as “limit case”, that the unchanged AOD at 0.55 mm is composed only by coarse mode particles (C-test). Fig. 9 shows the daily-average volume size distribution of the reference simulation (black dotted line) and of the C-test simulation (grey dotted line). As a consequence of the C-test the midvisible SSA is reduced by 0.05 (more solar absorption potential due to the larger particle size) and g is increased by 0.13 (stronger forward scattering by larger particles). Dubovik et al. (2002) have shown that SSA-AERONET values are retrieved with accuracy to the level of 0.03 for AOD (0.44 mm)  0.5. While, for AOD (0.44 mm)  0.2 the SSA accuracy level drops down to 0.05e0.07. Then, the DSSA value is close to the SSA accuracy level by AERONET since the daily AOD value at 0.44 mm is equal to 0.23 (Fig. 2). The retrieval errors of AERONET-derived g are in the range of 3e5% (Andrews et al., 2006). C-test results show that the aerosol solar effect decreases at the ToA and increases at the surface by assuming only coarse mode aerosol particles. This result is explained by the larger absorption potential (expressed by the single-scattering co-albedo (1
SSA) which increases by 83% for DSSA ¼ 0.05) and the reduced backscattering by larger size particles, as the mid-visible AOD remained unchanged. With larger size particles there are also stronger interactions at longer wavelengths resulting in larger terrestrial radiative impacts. Thus, aerosol T-DREs increase both at the ToA and surface: the T-DREsfc in this case nearly offsets the S-DREsfc and the T-DREToA warming dominates the S-DREToA cooling. Aerosol HRs in both solar and terrestrial domains are about doubled compared to the reference-simulation (Fig. 8, dashed-dotted lines

ref C fine mode coarse

0.12

6.3. Sensitivity test to the aerosol vertical distribution (D-test) To investigate the sensitivity of aerosol DREs to the aerosol vertical distribution (D-test), we have replaced the daily-average lidar-based profile (Fig. 10, solid line) with a simulated profile (Fig. 10, dotted line), which is representative of a well mixed aerosol layer extending up to 3 km. In this D-test relatively more aerosol optical depth fraction is placed at lower altitudes. Table 2 reveals that the clear-sky aerosol S-DRE is not very sensitive to the aerosol vertical profile both at the ToA and at the surface, in accordance with previous studies (e.g. Guan et al., 2010). The aerosol vertical distribution, however, can affect the S-DREs in cloudy environments. The D-test aerosol profile produces S-DREs, S-AF, and S-AFEs almost identical to corresponding values of the reference simulation. On the contrary, T-DREs are smaller at the ToA and slightly larger at the surface for the lower altitude placement of aerosol. Both S-HR and T-HR profile are modified (Fig. 11, dotted line) since they have a variation trend with altitude similar to the aerosol vertical profile. (SþT)-HR differences vary up w0.05 K day
1 (Fig. 12, black dotted line). Hence, the lack of knowledge of the actual aerosol vertical distribution can influence the accuracy of the aerosol HR. S-, T-, and all-wave-HRint differences are 2%, 20%, and 35%, respectively (Table 3). 6.4. Sensitivity test to the dependence on altitude of the aerosol size distribution (E-test) We have integrated polarization sensitive lidar and sun-sky photometer measurements to model the dependence on altitude of the aerosol size distribution. The column-averaged volume size distribution plotted in Fig. 9 is bi-modal and black-solid- and greydashed-lines represent the fine- and coarse-mode component, respectively. The E-test explores the sensitivity of aerosol DREs and HRs to the vertical distribution of fine- and coarse-mode particles.

7 6

ref D

5 4 3

3

2

dV/dlnr (μm /μm )

0.16

and Table 3). These results highlight the large impact of aerosol size on aerosol DREs.

Altitude (km)

absolute values of aerosol DREs and AFEs are smaller by about 6% in the solar domain (Table 2). The differences between the terrestrial n- and k-values from Sokolik and Toon (1999) and the GADS reference values are responsible for the increase (10%) of the TDREsfc. The percentage difference of the aerosol integrated all-wave HR is of 30% (Table 3). The A- and B-sensitivity tests highlight that HRs are more sensitive to the assumed n- and k-values than aerosol DREs.

351

2

0.08

1

0.04 0.00 5 6 7

2

0.1

3

4 5 6 7

1 Radius (μm)

2

3

0

4 5 6 7

10

0.00

0.10

0.20

0.30

AOT normalized Fig. 9. Daily-average size distribution of the reference simulation (black dotted line) and corresponding fine- (black solid line) and coarse-mode (grey dashed line) size distribution. The grey dotted line represents the daily-average C-test size distribution.

Fig. 10. Daily-average aerosol vertical profile of the reference simulation (solid line) and the D-test (dotted line).

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M.R. Perrone et al. / Atmospheric Environment 59 (2012) 344e354

7

a

b

S

6 5 Altitude (km)

(S+T) ref D E F

ref D E F T ref D E F

4 3 2 1

HRs are quite dependent on the vertical distribution of fine- and coarse-mode particles (Fig. 11a dashed lines). The higher altitude placement of coarse-mode particles decreases the downward terrestrial flux density to the surface and as a consequence, T-HRs with aerosols are less negative (less cooling) than T-HRs without aerosols below the coarse particle layer and are more negative (larger cooling) above 2.5 km where coarse-mode particles are placed. Then, we observe from Fig. 11a that the aerosol T-HR, which is the difference in HRs between an aerosol-laden and an aerosol-free atmosphere, is positive up to about 2 km from the ground and negative above. Huang et al. (2009) have also found that dust aerosols show a warming effect below the dust layers and a cooling near the top of the dust layers. S-HRs increase with the contribution of coarse-mode particles. (SþT)-HRs differences vary up to
0.1 K day
1 (Fig. 12, grey solid line). 6.5. Sensitivity test to the water vapour vertical profile (F-test)

0 -0.2

0.0

0.2

0.4 -0.2

0.0

0.2

0.4

Aerosol Heating Rate (K/day) Fig. 11. (a) Daily-average S and T aerosol heating rate of the reference simulation (solid lines) and test D (dotted lines), E (dashed lines), and F (dot-dash lines). (b) Dailyaverage (S þ T) aerosol heating rate of the reference simulation (solid lines) and test D (dotted lines), E (dashed lines), and F (dot-dash lines).

We assume as “limit case” that coarse- and fine-mode particles are due to non-spherical dust and background particles, respectively and that coarse- and fine-mode particles are placed above and below 2.5 km from the ground, respectively. Polarization sensitive lidar measurements and HYSPLIT 5-day analytical back trajectories of July 16, 2009 (http://ready.arl.noaa.gov/HYSPLIT.php) support both assumptions. S-DREs, S-AF, and S-AFEs are almost identical to corresponding values of the reference simulation (Table 2). Percentage differences do not exceed 2% of the reference simulation. T-DREs are instead larger at the ToA and smaller at the surface because of the higher altitude placement of coarse-mode particles (which dominate terrestrial aerosol radiative impacts). T- and S-

7 (S+T) A B C D E F

6

Altitude (km)

5

4

Aerosol DREs and HRs depend on aerosol optical and microphysical properties, but they are also affected by the levels of other atmospheric components such as the water vapour mixing ratio. Ground-based water vapour measurements at the lidar site and radiosonde measurements (see also http://esrl.noaa.gov/raobs/) at the meteorological station of Brindisi are used in this study to define, the water vapour vertical profile of the reference simulation up to 20 km altitude, as mentioned in section 2. To investigate the importance of the WVMR profile on aerosol DREs and HRs, we replaced the reference simulation profile (Fig. 5, black solid line) with the AFGL mid-latitude (30 e60 N) spring-summer WVMR profile (Fig. 5, grey solid line). Notable differences are lower AFGL water vapour content within the dust layer and up to w0.3 km from ground and larger AFGL water vapour content from w1.5 up to 2.5 km and above 4.2 km from ground. The switch to the AFGL WVMR profile has a negligible impact on aerosol S-DREs (Table 2) and decreases the aerosol T-DREToA and the T-DREsfc of 0.5 and 1.1 Wm
2, respectively. These results stem from heating associated in the near infrared (e.g. 1.4, 1.8, and 3 mm) and infrared absorption of water vapour (Kim et al., 2004) and hence, are likely due the larger column water vapour amount of the AFGL WVMR profile. Markowicz et al. (2003a) have shown that the increase of the column water vapour leads to a larger decrease in the surface aerosol DRE than that at the ToA, in accordance with model results of this paper. S-HRs are also weakly affected by the switch to the AFGL WVMR profile. T-HRs are mainly affected at altitudes smaller than w1 km from ground. The weaker aerosol terrestrial cooling below 1 km from ground is probably due to the larger column water vapour amount of the AFGL WVMR profile within this altitude range. 7. Summary and concluding remarks

3

2

1

0 -0.2

-0.1

0.0

0.1

0.2

Differences (K/day) Fig. 12. Aerosol (S þ T) heating rate differences of the reference simulation and the sensitivity test to refractive index (n and k) values (A- and B-test), aerosol size distribution (C-test), aerosol vertical distribution (D-test), vertical distribution of fine and coarse mode particles (E-test), and water vapour vertical distribution (F-test).

A Sahara dust outbreak which advected dust particles from north western Africa over south-eastern Italy in July 16, 2009 is analyzed. Polarization-sensitive lidar measurements, AERONET aerosol products, analytical backtrajectories, and model results support the advection of dust particles. Main goal of this study is to determine clear-sky instantaneous and daily average aerosol DREs and HRs in the S and T domain and to investigate the sensitivity of DREs and HRs to the properties and vertical distribution of aerosol particles. Instantaneous aerosol DREs and HRs undergo significant changes within few hours in July, 16 as a consequence of the high variability of the dust aerosol optical properties and vertical distribution. The aerosol S-DRE that is equal to
16.7 and
19.0 Wm
2 at the ToA and surface, respectively at 04:59 UTC,

M.R. Perrone et al. / Atmospheric Environment 59 (2012) 344e354

becomes
11.7 and
22.6 Wm
2 at the ToA and surface, respectively at 16:50 UTC. Thus, the S-AF increases from 2.3 up to 11 Wm
2. The increase of k with the time of the day is mainly responsible for this result. Dust particles are mostly responsible in the early morning of July, 16 for S-HRs y 0.18 K day
1 and T-HRs y
0.05 K day
1 between 3 and 4.5 km from ground. The dust layer moves downward with the time of the day and as a consequence at 16:50 UTC, the S-HR increases up to w0.3 K day
1 at 2.5 km from the ground and decreases above 3.5 km. The daily-average aerosol T-DRE that is equal to 1.8 and 6 Wm
2 at the ToA and surface, respectively, offsets the (negative) S-DRE of 34 and 51% at the ToA and surface respectively. Aerosol S- and T-HR profiles are linked to the aerosol vertical distribution and the daily average S- and T-HR integrated over the whole aerosol column is 0.5 and
0.3 K day
1, respectively. We have conducted a series of sensitivity tests to evaluate the sensitivity of daily-average aerosol DREs and HRs to the uncertainty of n and k values, aerosol size distribution, aerosol vertical distribution, vertical distribution of fine and coarse mode particles, and water vapour vertical profile. Aerosol DREs and HRs are quite sensitive to n and k values. In particular, the increase of the imaginary refractive index in the S domain, has a large impact on the aerosol S-DRE at the surface and AF while, it has a negligible impact on the aerosol S-DREToA. Coarse-mode particle uncertainties have a large impact on aerosol DREs and HRs, both in the S and T domain. The solar aerosol-cooling-effect decreases at the ToA and increases at the surface as the contribution of coarse particles increases. Aerosol T-DREs increase with the contribution of coarse mode particles both at the ToA and surface. Hence, the lack of knowledge of the real contribution of coarse mode particles can imply an underestimation of the (SþT)-DRE at the ToA and surface, respectively. The increase of the coarse-mode particle contribution also translates into an increase of the (positive) S-HRint and a decrease of the (negative) T-HRint. Uncertainties on the aerosol vertical profile have a negligible impact on solar DREs, AF, and AFEs. While, the aerosol T-DREs becomes smaller at the ToA and slightly larger at the surface if a lower altitude placement of aerosol particles occurs. Aerosol Sand T-HRs have a variation trend with the altitude similar to the aerosol vertical distribution and as a consequence, they are significantly affected by aerosol vertical profile uncertainties. The sensitivity test on the vertical distribution of fine- and coarsemode particles reveals that S-AF, S-DREs, and S-AFEs are not very sensitive to the vertical placement of fine- and coarse-mode particles. T-DREs become instead larger at the ToA and smaller at the surface for the higher altitude placement of coarse-mode particles. Aerosol T- and S-HRs are quite dependent on the vertical distribution of fine- and coarse-mode particles. Finally, it is shown that the uncertainties of the water vapour profile have a large impact on aerosol T-DREs and a quite week impact on aerosol S-DREs. In conclusion, the paper contributes to the evaluation of DREs and HRs by desert dust particles at Mediterranean sites and highlights the importance of understanding how S- and T-DREs and HRs act together and alter the energy budget at the ToA and surface and within the aerosol column. Compared to the S aerosol DRE, the T aerosol DRE has received less consideration, even if aerosols with large particles, such as desert dust, can exert significant T-DREs. It is shown that the modelling of aerosol DREs and HRs in the S and T domain, respectively requires an accurate specification of aerosol optical and microphysical properties and of their changes within the atmospheric column and with the time of the day. These studies add to the broader science even if a case study has been used to perform radiative transfer calculations and can be of large scientific

353

interest since the Mediterranean is one of the most responsive regions to climate change. Acknowledgements Dr. P. Martano from ISAC-CNR Department of Lecce (Italy) is kindly acknowledged for providing the net radiometer data. Dr. S. Di Sabatino from “Laboratorio di Micrometeorologia, Dipartimento di Scienza dei Materiali”, University of Salento is kindly acknowledged for providing the ground meteorological data. References Anderson, et al., 1986. AFGL Atmospheric Constituent Profiles (0e 120 km). In: Rep. AFGL-TR-86-0110. Air Force Geophys. Lab., Hanscom Air Force Base, Mass. Andrews, et al., 2006. Comparison of methods for deriving aerosol asymmetry parameter. J. Geophys. Res. 111, D05S04. http://dx.doi.org/10.1029/2004 JD005734. Bergamo, et al., 2008. Monthly-averaged anthropogenic aerosol direct radiative forcing over the Mediterranean from AERONET derived aerosol properties. Atmos. Chem. Phys. 8, 6995e7014. Carlson, T.N., Benjamin, S.G., 1980. Radiative heating rates for Sahara dust. J. Atmos. Sci. 37, 193e213. Derimian, et al., 2008. Radiative properties of aerosol mixture observed during the dry season 2006 over M’Bour, Senegal (African Monsoon Multidisciplinary Analysis campaign). J. Geophys. Res. 113, D00C09. http://dx.doi.org/10.1029/ 2008JD009904. De Tomasi, F., Perrone, M.R., 2003. Lidar measurement of tropospheric water vapour and aerosols profiles over southern Italy. J. Geophys. Res. 108, 4286e4297. De Tomasi, et al., 2006. Height and seasonal dependence of aerosol optical properties over south-east Italy. J. Geophys. Res. 111, D10203. http://dx.doi.org/ 10.1029/2005JD006779. Di Biagio, et al., 2009. Measurements of Mediterranean aerosol radiative forcing and influence of the single scattering albedo. J. Geophys. Res. 114, D06211. http:// dx.doi.org/10.1029/2008JD011037. Di Biagio, et al., 2010. Large atmospheric shortwave radiative forcing by Mediterranean aerosols derived from simultaneous ground-based and spaceborne observations and dependence on the aerosol type and single scattering albedo. J. Geophys. Res. 115, D10209. http://dx.doi.org/10.1029/2009JD012697. Dubovik, et al., 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements. J. Geophys. Res. 105 (D8), 9791e9806. Dubovik, et al., 2002. Non-spherical aerosol retrieval method employing light scattering by spheroids. J. Geophys. Res. Lett. 29. 54-61e54-4. Dubovik, et al., 2006. Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. J. Geophys. Res. 111, D11208. http://dx.doi.org/10.1029/2005JD006619. Dufresne, et al., 2002. Longwave scattering of mineral aerosol. J. Atmos. Sci. 59, 1959e1966. García, et al., 2008. Validation of AERONET estimates of atmospheric solar fluxes and aerosol radiative forcing by ground-based broadband measurements. J. Geophys. Res. 113, D21207. http://dx.doi.org/10.1029/2008JD010211. Gomez-Amo, et al., 2011. The June 2007 Sahara dust event in the central Mediterranean: observations and radiative effects in marine, urban and sub-urban environments. Atmos. Environ. 45, 5385e5393. Guan, et al., 2010. Sensitivity of shortwave radiative flux density, forcing, and heating rate to the aerosol vertical profile. J. Geophys. Res. 115, D06209. http:// dx.doi.org/10.1029/2009JD012907. Guerrero-Rascado, et al., 2009. Extreme Sahara dust event over the southern Iberian Peninsula in september 2007: active and passive remote sensing from surface and satellite. Atmos. Chem. Phys. 9, 8453e8469. Haywood, et al., 2011. Observations and modelling of the solar and terrestrial radiative effects of Saharan dust: a radiative closure case-study over oceans during the GERBILS campaign. Q. J. R. Meteorol. Soc. 137, 1211e1226. Holben, et al., 1998. AERONET e a federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 66, 1e16. Huang, et al., 2009. Taklimakan dust aerosol radiative heating derived from CALIPSO observations using the Fu-Liu radiation model with CERES constraints. Atmos. Chem. Phys. 9, 4011e4021. Kim, et al., 2004. Observation of enhanced water vapour in Asian dust layer and its effect on atmospheric radiative heating rates. Geophys. Res. Lett. 31, L18113. http://dx.doi.org/10.1029/2004GL020024. Kleidman, et al., 2005. Comparison of moderate resolution imaging spectroradiometer (MODIS) and aerosol robotic network (AERONET) remote-sensing retrievals of aerosol fine mode fraction over ocean. J. Geophys. Res. 110, D22205. http://dx.doi.org/10.1029/2005JD005760. Koepke, et al., 1997. Global Aerosol Dataset. In: Report N 243. Max-Plank-Institut für Meteorologie, Hamburg, 44 pp. Lemaitre, et al., 2010. Radiative heating rates profiles associted with a springtime case of Bodele and Sudan dust transport over Africa. Atmos. Chem. Phys. 10, 8131e8150.

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