Remote sensing measurements of aerosols using depolarization Raman lidar
systems from four EARLINET (European Aerosol Research Lidar Network) stations
are used for a comprehensive analysis of Saharan dust events over the
Mediterranean basin in the period 2014–2017. In this period, 51 dust
events regarding the geometrical, optical and microphysical properties of
dust were selected, classified and assessed according to their radiative forcing
effect on the atmosphere. From west to east, the stations of Granada,
Potenza, Athens and Limassol were selected as representative Mediterranean
cities regularly affected by Saharan dust intrusions. Emphasis was given on
lidar measurements in the visible (532 nm) and specifically on the
consistency of the particle linear depolarization ratio (
The Saharan desert is one of the major dust sources globally, with dust advection to the Mediterranean countries being modulated by meteorology along rather regular seasonal patterns (Mona et al., 2012). For instance, in the western Mediterranean region, the African dust occurrence is higher in summer (Salvador et al., 2014), even though some extreme events might also occur in winter (e.g., Cazorla et al., 2017; Fernández et al., 2019), while in the central Mediterranean region, spring and summer are, usually, associated with dust aerosol loads extending up to altitudes of 3–4 km (Barnaba and Gobbi, 2004). In the eastern Mediterranean, the main dust transport occurs from spring to autumn (Papayannis et al., 2009; Nisantzi et al., 2015; Soupiona et al., 2018) as a result of the high cyclonic activity over northern Africa during these periods (Flaounas et al., 2015). Considering also that the Mediterranean basin is a region of high evaporation, low precipitation and remarkable solar activity, the transportation of aerosols accompanied by aging and mixing processes make this area a study of interest for present and future climate change effects (Michaelides et al., 2018).
It is well documented that mineral dust highly influences the atmospheric radiative balance through scattering and absorption processes (direct effects), as well as cloud nucleation, formation and lifetime (indirect effects), as summarized in IPCC (2014). Considerable uncertainties in quantifying the global direct radiative effects of aerosols arise from the variability of their spatiotemporal distribution and the aging and mixing processes that can affect their optical, chemical and microphysical properties and influence many processes that modulate regional climate. Therefore, the magnitude and even the sign of the dust aerosol solar radiative forcing are highly uncertain as they strongly depend on their optical properties, their size distribution and their complex refractive index (CRI) values. Papadimas et al. (2012) reported that the aerosol optical depth seems to be the main parameter for modifying the regional aerosol radiative effects (under cloud-free conditions) and that on an annual basis, aerosols can induce a significant “planetary” cooling over the broader Mediterranean basin. Other studies (Quijano et al., 2000; Tegen et al., 2010) have shown that the presence of clouds and the surface albedo are also unquestionable parameters affecting the net solar radiative transfer at the top of the atmosphere. However, a comprehensive analysis from ground-based aerosol optical properties to vertical profiles of short- and longwave (SW and LW) radiation estimations in the Mediterranean region has been reported so far only in a few papers (Sicard et al., 2014; Meloni et al., 2003, 2015; Valenzuela et al., 2017; Gkikas et al., 2018).
Although there have been a lot of studies about Saharan dust optical properties based on the lidar technique (Landulfo et al., 2003; Ansmann et al., 2009; Papayannis et al., 2009; Córdoba-Jabonero et al., 2011; Tesche et al., 2011; Mona et al., 2012; Groß et al., 2013; Navas-Guzmán et al., 2013; Granados-Muñoz et al., 2016; Mandija et al., 2016, 2017; Rittmeister et al., 2017; Soupiona et al., 2018), systematic long-term statistical studies are quite scarce since few aerosol depolarization data are available covering long periods. Saidou Chaibou et al. (2020) address the importance of dust effects in climate studies in order to improve the accuracy of climate predictions. As they mention, even if improved assessment of dust impact on climate requires continuous observations from both satellites and ground-based instrument networks, the use of climate models is also crucial to improve our understanding of dust distribution, its properties and its impact on the radiation budget. In an earlier study, Pérez et al. (2006) proposed that a regional atmospheric dust model, with integrated dust and atmospheric radiation modules, represents a promising approach for further improvements in numerical weather prediction practice and radiative impact assessment over dust-affected areas, especially in the Mediterranean. Hence, an in-depth study of the role of aerosols in radiative forcing over different regions in the Mediterranean basin is still needed. While a synergy of ground-based lidar measurements and modeling seems very promising for obtaining radiative forcing estimations of dust aerosols, the use of inputs from regional models could also contribute to such estimations in areas where measurements are unavailable.
This paper aims to fill some of the aforementioned gaps by combining statistical lidar data of aerosol optical and microphysical properties with radiative transfer estimations and is organized as follows. A brief summary of the four selected EARLINET (European Aerosol Research Lidar Network) Mediterranean stations is given in Sect. 2, along with the data selection of the dust cases. Section 3 includes the description of the methodologies applied and the tools and models used for retrieving the aerosol optical and microphysical properties and their radiative forcing. The evaluation of the retrieved aerosol mass concentration profiles and of the ground-level radiation are also presented. The results of the aerosol optical, geometrical and microphysical properties of the individual dust layers and the clusters, as well as the relevant radiative forcing calculations over the studied areas, are discussed in Sect. 4. Finally, concluding remarks are given in Sect. 5.
Station name, location, lidar setup and relevant references of the four selected EARLINET stations.
The European Aerosol Research Lidar Network (EARLINET;
Four EARLINET stations affected by typical Saharan dust intrusions in the
Mediterranean were selected (listed from west to east): Granada (Spain),
Potenza (Italy), Athens (Greece) and Limassol (Cyprus). A 4-year
(2014–2017) common period of aerosol depolarization Raman lidar data
obtained at 532 nm was selected for this analysis. Table 1 summarizes the
basic information about these lidar systems for each location. Except the
Limassol station that provides data only at 532 nm, the other three stations
are equipped with a multiwavelength lidar system able to provide extensive
aerosol properties at multiple wavelengths, namely three
96–120 h backward trajectories for air masses arriving over
Dusty cases analyzed in this study were selected based on the values of the
aerosol optical properties
Moreover, a careful investigation of the air mass origin and dust transport
path was performed by means of backward trajectory analysis. This analysis
was carried out using the HYbrid Single-Particle Lagrangian Integrated
Trajectory (HYSPLIT) model (
Thus, we ended up with 51 individual cases in total, of 30 min to 1 h averaged lidar profiles each (15 for Granada, 18 for Potenza, 12 for Athens and 6 for Limassol). For the region of Cyprus, the situation is more complex since Middle East dust outbreaks also occur frequently in addition to the Saharan dust events (Nisantzi et al., 2015; Kokkalis et al., 2018; Solomos et al., 2019). On top of that, dust particles originating from the Middle East proved to have different lidar ratio values than the corresponding observations over the Saharan desert (Mamouri et al., 2013; Kim et al., 2020). Taking this into account, dust cases over the Limassol station originating from Middle East regions were excluded from our study.
The air mass trajectory analysis based on HYSPLIT for each station reveals the origin of each observed layer (Fig. 1). In the majority of cases, air masses originate from western and northwestern Africa (Morocco, Mauritania, Algeria and Tunisia). At first glance, two occurrences seem to dominate: (i) trajectories that travel directly from the source to the observation stations and (ii) trajectories that circulate over the Mediterranean or the Atlantic Ocean (for the Granada and Potenza cases), Europe and the Balkans or even Turkey (for the Limassol and Athens cases) before reaching the observation stations.
In order to perform simulations for further investigating the behavior of the transported dust aerosols and their impacts, we used different methods with a variety of tools and models. In this section, we present our efforts for retrieving vertical dust mass concentration profiles, aerosol microphysical properties and radiative forcing results. The simulations were also partly validated with ground-based radiation measurements.
To retrieve the aerosol dust mass concentration profiles, we used the
Assumed (
The SphInX software tool provides an automated process to carry out calculations
from lidar data to obtain the aerosol microphysical properties and further
to statistically evaluate the inversion outcomes. It has been developed at
the University of Potsdam (Samaras, 2016) within the Initial
Training for atmospheric Remote Sensing (ITaRS) project (2012–2016). SphInX
operates with expendable precalculated discretization databases based on
spline collocation and on lookup tables of scattering efficiencies using
T-matrix theory (Rother and Kahnert, 2009). This is to avoid the
computational cost which would otherwise limit the microphysical retrieval
to an impractical point. The complex refractive index (CRI) is fed to the
software separately for the real and imaginary parts, which then constitutes
a grid combining the following default values: real part (RRI)
The BSC-DREAM8b model (Basart et al., 2012),
operated by the Barcelona Supercomputer Center (BSC-CNS;
The aerosol effects on solar and terrestrial radiation are usually
quantified through the so-called aerosol radiative forcing (ARF). The
ARF defined here as the perturbation in flux in
the atmosphere caused by the presence of the dusty layers in relation to
that calculated under clear-sky conditions can be expressed as
(Quijano et al., 2000; Sicard et al., 2014; Mishra et al., 2014)
In this study, the downwelling and upwelling shortwave (280–2500 nm) and
longwave (2.5–40
A set of four simulations was carried out per case of the studied dust
events. The first two simulations refer to clear-sky atmospheres with
background/baseline aerosol conditions (default properties: rural type
aerosol in the boundary layer, background aerosol above 2 km, spring–summer
conditions and a visibility of 50 km; index “clear” in Eq. 2), the first
for the SW and the second for the LW range, since these ranges are treated
separately by libRadtran. The remaining two simulations correspond to the dust-loaded atmosphere, again, the one for the SW range and the other for the LW
range, respectively, for which the vertical profiles of the dusty layers
were used as additional inputs (index “dusty” in Eq. 2). These inputs have
been obtained by three different schemes: (a) vertical mass concentration
profiles simulated by the BSC-DREAM8b model, (b) vertical mass concentration
profiles of only the dust component as calculated from Eq. (1) (mass
The flowchart in Fig. 2 depicts these three schemes applied to create the
input files for the dust-loaded atmospheric conditions used in the libRadtran
software package (Emde et al., 2016).
Scheme A refers to the dust mass concentration as estimated by BSC-DREAM8b
over the studied sites. In Scheme B, only the dust vertical distribution is used
as input (based on the separation of the
For all these schemes in this study, 30 vertical levels have been used
between the ground and 120 km height, with a spatial vertical resolution of 0.5 km starting from ground level (BOA) to 2 km and from 5 to 10 km and a
resolution of 0.2 km from 2 to 5 km, due to the presence of the dust layers
within this height range and additionally at the heights of 20 and 120 km
(TOA). All simulations were performed for three different solar zenith
angles (SZAs), 25, 45 and 65
Flowchart of the three schemes used to retrieve simulations of irradiances using the libRadtran software package.
The libRadtran irradiance outputs have been validated against reference solar irradiance pyranometer measurements at the Earth's surface (Kosmopoulos et al., 2018). For this study, solar radiation data measured by pyranometers were available only for the Granada and Athens stations. The evaluation was performed using cloudless time periods only. The reference solar radiation dataset consists of 1 min simultaneous measurements of horizontal global and diffuse irradiance measured with two CMP11 pyranometers in Granada and two CMP21 pyranometers in Athens (located at National Observatory of Athens actinometric station in the Penteli area, 10 km from NTUA). These pyranometer models, both manufactured by Kipp & Zonen, have a black-coated thermopile acting as a sensor which is protected against meteorological conditions by two concentric hemispherical domes. They both comply with the International Organization for Standardization (ISO) 9060 (1990) criteria for an ISO secondary standard pyranometer, being classified as “high quality” according to the World Meteorological Organization (WMO) nomenclature (WMO, 2018). Additionally, the corresponding pyranometer measuring the diffuse component was mounted on a shading device to block the direct irradiance and prevent it from reaching the sensor. In this study, the shading devices employed were a Solys2 sun tracker and a CM121 shadow ring, at Granada and Athens, respectively. For those diffuse irradiance measurements taken using a shadow ring, the model proposed by Drummond (1956) has been applied in order to correct for the diffuse radiation intercepted by the ring, as suggested by the manufacturer (Kipp & Zonen, 2004).
Taylor diagram of the case-by-case vertical mass concentration
simulated by BSC-DREAM8b model against the lidar-retrieved ones. The black
point (1,0) represents the calculated lidar data. The azimuthal angle
presents the correlation coefficient (
Statistical metrics for the modeled global irradiance values versus the reference pyranometer measurements for Granada and Athens and the three schemes applied.
Before using the vertical dust mass concentrations profiles retrieved from
(i) BSC-DREAM8b model simulations (Scheme A) and (ii) lidar measurements as
calculated from Eq. (1) (mass
Figure 3 shows a Taylor diagram of the mass concentrations simulated by
the BSC-DREAM8b model against the lidar-retrieved ones. The azimuthal angle
presents the correlation coefficient, the radial distance presents the
normalized standard deviation (SD) of each point and the root mean square error
(RMSE) is proportional to the distance from the point on the
By further comparing the modeled mass vertical profiles to the ones
calculated by lidar, we report that the mean center of mass (in km)
estimated from BSC-DREAM8b profiles is 0.6 km lower than the one calculated
from the lidar measurements (
The evaluation of the performance of the model was undertaken by statistical
means. The relative root mean square error (rRMSE), the relative mean bias
error (rMBE), the correlation coefficient (
Mean values along with the standard deviation of
For each case studied, the mean
Considering Granada's station as representative of the western Mediterranean
region, Potenza of the central Mediterranean region and Athens and Limassol
stations of the eastern Mediterranean region, a dust aerosol mode
classification per region can be made. For this purpose, the mean
AOT
In terms of the aerosol size distribution, the scatter plot of Fig. 5
allowed
Based on the High Spectral Resolution Lidar (HSRL) classification presented
by Groß et al. (2013),
the intensive aerosol quantities
Table 4 summarizes the mean values of the aerosol geometrical, optical and
microphysical properties of the three identified clusters along with their
SD (5 cases for BB and Saharan dust, 8 cases for Saharan dust, 29 cases for
mixed Saharan dust). A synergistic approach of HYSPLIT (trajectories of 120 h backward for each case) and Google Earth (distance calculator) tools
allowed us to estimate the distance traveled (in km) to the respective
sites and the mixing hours per cluster. Specifically, the term of mixing
refers to the hours the air masses traveled after leaving the African
continent. We can see that the Saharan dust cluster presents the lowest
mixing with other air masses (
Mean values of optical, geometrical and microphysical properties of the three identified clusters, along with their standard deviation (SD). Zero SD indicates no variability in the corresponding retrieved parameter. The term “mixing” refers to the hours the air masses traveled after leaving the African continent.
Concerning the aerosol optical properties, the
We also summarize the changes in mean microphysical properties estimated
with the SphInX tool for all the cases of each of the three identified clusters.
The BB and Saharan dust cluster has a lower mean
As mentioned previously, there is a shortage of papers in the literature about the role of dust in the Earth's radiation budget. Since very few in situ measurements of ARF effects and heat fluxes are available, especially in the Mediterranean (Bauer et al., 2011; Meloni et al., 2018), we are restricted to performing simulations to quantify the role of dust aerosols in radiative forcing in the studied regions. The mean ARF is calculated during this simulation, running the libRadtran radiation code twice: with (index “dusty” in Eq. 2) and without (index “clear” in Eq. 2) the presence of free tropospheric dusty aerosol layers. For all cases, the vertical profiles of ARF starting from ground level/bottom of atmosphere (BOA) up to the top of atmosphere (TOA) in the SW and LW ranges were simulated using the three aforementioned schemes.
A negative forcing of aerosols both at the BOA and TOA is noted in the SW
range, as presented in Fig. 7a, which depicts the mean ARF of all cases per
scheme, over the Mediterranean Basin. Our results indicate a presence of
less absorbing aerosols, thus having a cooling behavior. Depending on the
dust optical properties and load intensity, ARF values at the BOA range from
Variations among these values are expected since they strongly depend on the
different AOTs, mass estimations and extinction profiles. Estimations
retrieved from Scheme B are expected to give higher values compared to those
given from Scheme A, as also revealed by Fig. 3. The ARF in the LW spectral
region is opposite in sign and significantly lower in absolute values than
in the SW region. The difference between the TOA and BOA ARF, with the
former only weakly perturbed and the latter much stronger, can
be attributed to the heating within the troposphere, since the presence of
the dust aerosols mainly leads to a displacement of the surface's radiative heating
into the dusty layer. We also noticed that the low values of the reflected
solar flux are partially offset by the absorption of upwelling LW radiation.
Finally, in the LW spectral region, the mean ARF values at the BOA (Scheme
A:
The mean net heating rate within the atmosphere, calculated by adding
algebraically both rates in the SW and LW spectral ranges, is presented in
Fig. 7b. Here, the net heating rate is clearly dependent on the available
solar radiation and increases with SZA due to the low incoming solar
radiation reaching the BOA during afternoon hours (SZA 65
Mean values of
In order to further explain the difference of sign in the net heating rate
of Scheme C, compared to the two others presented in Fig. 7b, we plotted the
aforementioned parameter along with the base layer height, the AOT
Net heating rate values per case of Scheme C estimated at BOA,
25
Figure 9a–c depict the same results as in Fig. 7a but for each of the
three identified clusters: BB and dust, mixed Saharan dust and Saharan dust.
The ARF in the SW range is negative both in the BOA and TOA for all clusters
and is dominated by large dust particles for the cluster of the Saharan dust
episodes (see Table 4; Fig. 9c), (Scheme A:
Hence, even though the studied cases included in the Saharan dust cluster usually have higher mass concentration values than the other cases, as predicted by BSC-DREAM8b (Scheme A), the model still seemingly underestimates the intensity of strong transported dust episodes over the observation stations. In contrast, Scheme C is the most sensitive to the mixing state of the aerosol layers. To explain this result one should consider that on the one hand, spheroidal particles such as dust have larger dimensions than spherical ones such as BB aerosols and thus lead to larger AOTs (Haapanala et al., 2012) and consequently to increased negative ARF, and on the other hand, Schemes A and B involve greater assumptions concerning dust particles than Scheme C.
Mean values of SW and LW ARF at BOA and TOA, along with the
SD for the three schemes applied regarding the mixing state, namely
Vertical profiles of
Finally, our interest is focused on the vertical ARF profiles from the
surface (a.s.l.) up to 10 km height in the free troposphere, where airborne
dust is usually found, as estimated by Scheme C at 45
The characteristics of aerosol layers dominated by dust optical,
geometrical and radiative properties over the Mediterranean region were
presented in this study. A total of 51 independent aerosol lidar
measurements of Saharan dust events, studied over four southern European
cities, were carefully selected and analyzed. The dust layers were usually
observed between
Despite the numerous individual studies, the uncertainty in estimating the
aerosols' effect on climate change remains high. Therefore, coordinated and
simultaneous studies using data from observation sites operating
continuously, such as the EARLINET database, are necessary for investigating
the climatic effect of aerosols on a larger scale. Three schemes have been
implemented in our study to evaluate the ARF during the selected dust
outbreaks: the model mass concentrations by BSC-DREAM8b (Scheme A), the
vertical mass concentrations calculated from the dust-only component of the
Lidar-derived Schemes B and C are used here as input methods in libRadtran
simulations, since not many techniques have been widely used for retrieving
the ARF using lidar vertical measurements as input. Their outputs are
compared to the ones retrieved from Scheme A (based on BSC-DREAM8b model).
On the one hand, Scheme B gives the opportunity to calculate only the DRF,
even though many assumptions and constants are included in the calculation
of the dust mass concentration values. On the other hand, Scheme C is more
direct, since the
The ARF variations are strong (of the order of 75 %) and result from
significant changes in the lidar-retrieved optical properties due to the
different intensities of the studied cases (
The systematic use of remote sensing vertical profiling measurements as input to radiative transfer models is stressed in this study, creating an essential tool allowing the estimation of the radiative effects produced by different aerosol types such as dust and its mixtures on a regional and a global scale. A further investigation of aerosols' mixing state is needed since not only their optical but also their microphysical properties and radiative forcing can strongly vary, depending on the mixing types. Furthermore, we recommend that the use of remote and in situ measurements in next-generation state-of-the-art dust cycle models for the ARF should be intensified.
The aerosol lidar profiles used in this study are available upon registration from the EARLINET web page at
OS conducted the research process and performed the analysis. GSH, POA, MM, CAP, NP, SG, REM and BP contributed to the data curation and preprocessing. SS provided the SphInX software tool. OS and RF performed the libRadtran simulations. AP and PK supervised the project and helped with paper preparation. All authors contributed to the writing of the manuscript.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Dust aerosol measurements, modeling and multidisciplinary effects (AMT/ACP inter-journal SI)”. It is not associated with a conference.
The authors also acknowledge the BSC-DREAM8b model, operated by the Barcelona Supercomputing Center, the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT model, and Google Earth and the AERONET for high-quality sun and sky photometer measurements. The Biomedical Research Foundation of the Academy of Athens (BRFAA) is acknowledged for the provision of its mobile platform to host the NTUA AIAS lidar system.
This research was funded by the COST Action “InDust” under grant agreement CA16202, supported by COST (European Cooperation in Science and Technology). Ourania Soupiona's research has been financed through a scholarship for PhD Candidates from the General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI). Alexandros Papayannis, Romanos Foskinis and Christina-Anna Papanikolaou received support from the project “PANhellenic infrastructure for Atmospheric Composition and climatE change” (MIS 5021516), which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund). Rodanthi-Elisavet Mamouri acknowledges the ERATOSTHENES Centre of Excellence and the “EXCELSIOR” H2020 Widespread Teaming project that has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement no. 857510 and from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development. The EARLINET lidar data were made available through the financial support by the ACTRIS Research Infrastructure Project funded by the European Union's Horizon 2020 Research and Innovation programme under grant agreement no. 654169.
This paper was edited by Matthias Tesche and reviewed by two anonymous referees.