Satellite observations of Saharan dust events in the Mediterranean and
its effect on surface phytoplankton biomass
G. Volpe (1), R. Sciarra (1), G. Liberti (2), F. D'Ortenzio (3), R. Santoleri (1),
A. Papadopoulos(4,5), P. Katsafados (4), and G. Kallos(4)
(1) Istituto per le Scienze dell’Atmosfera e del Clima - Sezione di Roma, Roma, Italy; (2) Istituto per
le Scienze dell’Atmosfera e del Clima, Bologna, Italy; (3) Stazione Zoologica ¸ “A. Dohrn”, Naples,
Italy; (4) Division. of Applied Physics, School of Physics, University of Athens, Athens, Greece.
(5)
National Centre for Marine Research, Athens, Greece
gianluca@ekman.ifa.rm.cnr.it
ABSTRACT
Many studies indicate that the atmosphere is a significant and in some cases the dominant pathway by which specific
elements are transported from the land to the open sea. The Mediterranean Sea is a semi-enclosed basin, that
continuously receives anthropogenic substances from the industrialized European country, and sporadically, from the
arid region of the Sahara desert, nearly the 90% of the total amount of aerosols that reach the sea surface. The
Mediterranean is a predominantly oligotrophic basin with areas of high productivity limited to areas influenced by
runoff, rivers or upwelling. In situ biogeochemical measurements indicate that atmospheric deposition can induce
significant productivity changes. The present work aims to use SeaWiFS satellite data and the SKIRON atmospheric
model to provide an estimate of the temporal and spatial variability in the atmospheric forcing (dust events) and in the
marine biological response (blooms), and to evaluate the overall contribution of these Saharan dust events to the fertility
of the Mediterranean Sea. Although biological dynamic is meanly driven by the circulation features of the basin, results
show that the atmospheric nutrient deposition gives some evident response in the biological activity.
Keywords: dust deposition, chlorophill_a, SeaWiFS, SKIRON, Mediterranean Sea, Ocean Colour, blooms
1. INTRODUCTION
To which extent does atmospheric deposition impact on the biogeochemistry of the Mediterranean Sea?
The Mediterranean Sea is characterized by low nutrients (N, P, Fe etc) concentrations. This condition, common to most
of the open oceans, does not support high levels of phytoplankton productivity 1 (figures 1). However the presence of
numerous meso-scale structures and of coastal zones contribute to enhance the production of the system biomass. The
western and eastern basins nutrient budget depends on water exchanges at the Gibraltar and Sicily Straits respectively,
on river runoff, on atmospheric deposition and on the complex circulation dynamic in both sub-basins.
Moreover the anti-estuarine dynamic at the Strait of Gibraltar implies a nutrient depleted inflow of Atlantic subtropical
water, which contributes to the oligotrophic condition of many parts of the basin. Furthermore, because of the
Mediterranean nutrient reach outflow, the nutrient balance can be only solved through the atmospheric and river-runoff
contributions.
Nearly 90% of total amount of phosphorus, the most limiting element in the Mediterranean, comes from Atlantic and
rivers inputs 3, the rest is given by the atmospheric contribution. Nevertheless, in some areas and in particular weather
conditions (stratification condition), the atmospheric inputs become the main nutrient source for the euphotic surface
layer 4. The extent to which the atmosphere represents an important pathway for various pollutants and natural
substances from Europe and North Africa towards the open Mediterranean Sea and subsequently affects its
biogeochemistry remains largely unknown. While nutrients enhance the marine productivity, other substances, such as
lead, mercury and some chlorinated hydrocarbons, when carried into the sea by atmosphere are potentially harmful to
marine biological systems. Atmospheric inputs thus exert many different effects on the Mediterranean environment.
The Sea-viewing Wide-Field-of-View Sensor (SeaWiFS) objective, as all ocean color sensors, is to assess the
phytoplankton abundance by measuring the seawater leaving radiances, in order to study spatial-temporal variability and
long-term changes in the ocean with possible links to climate change. Moreover, high spatial and temporal resolution of
SeaWiFS, the evolution of seasonal to episodic phytoplankton blooms such as those related to dust events could also be
investigated. Unfortunately, more than 90% of the signal measured by the sensor is due to the influence of the
atmosphere, therefore calculation of atmospheric optical properties is essential in determining the atmospheric
contribution that must be removed from the total radiance captured by the sensor to evaluate the marine contribution.
Here we propose to use atmospheric parameters correction geophysical parameters (such as Aerosol Optical Thickness,
Ångstrom coefficient, aerosol models) to describe the atmospheric aerosol content and to monitor Saharan dust events
occurring in Mediterranean Sea.
In this paper, SeaWiFS data relative to the year 2000 were processed to obtain atmospheric and oceanic parameters and
were used to monitor Saharan dust events and ocean phytoplankton blooms on daily basis. SKIRON model outputs have
been used to localize the Saharan dust deposition area of each event in order to evaluate the biological impact by
observing the evolution of the SeaWiFS retrieved chlorophyll_a and to eventually estimate the time response of
phytoplankton to atmospheric nutrient inputs.
0
0.05
0.07
0.1
0.15
[Chl_a] mg m-3
0.3
0.5
1.5
5
340
450
Fig.1a. SeaWiFS Chlorophill_a annual mean over 2000 2.
0
110
130
160
200
g C m-2 year-1
250
280
Fig.1b. Modelled primary production integrated over the whole 2000 2.
3. DATA SET
3.1 SeaWiFS
All the available Level 1A SeaWiFS data (over 800 passes) covering the Mediterranean Sea for the year 2000 were
extracted from the ISAC satellite archive and processed up to Level 2 (L2) with the last version of the SeaDAS
(SeaWiFS Data Analysis System) software v.4.0B 5, available from NASA, that uses the new calibration coefficients (reprocessing # 3). The processing was carried out according to the following standard NASA processing options:





Multi-scattering atmospheric correction
Aerosol model selection based on the analysis of the 765 and 865 nm radiances
Siegel 6 NIR iterations
OC4v4 algorithm 7 chlorophyll computation
Application of land and stray-light flags.
Twenty-seven geophysical products with all 24 L2 quality flags were produced in HDF format and stored on tape. Then
all the geophysical products were remapped in equirectangular projection over the area 42-49N, 10W-42.3E.
In the present work we used the Chlorophill_a, the Aerosol optical Thickness, Aerosol Index (AI), the aerosol
atmospheric correction models and the True color images.
SeaWiFS Chlorophyll maps were generated using Ocean Color 4-band algorithm version 4 (OC4v4). The OC4v4 is an
empirical equation based on relating remote sensing reflectances Rrs at different bands to chlorophyll_a concentration
measurements by means of the following equation:
Chl  10( a0 a1Ra2R
2
a3R3 a4 R 4 )
.
1)
where R is the log10 of ratio of Rrs measured either at 443 and 555 nm, or 490 and 555 nm, or 510 and 555 nm,
depending on its value (the maximum is chosen). The coefficients are reported in the following table.
Table 1
Algorithm
OC4v4
a0
a1
a2
a3
a4
R
0.366 -3.067 1.930 0.649 -1.532 log10 (Rrs443 > Rrs490 > Rrs510)/Rrs555)
The aerosol optical thickness  is a dimensionless quantity that describes the absorption and scattering properties of the
atmosphere. These phenomena are due to the interaction of photons with air molecules and with aerosols. The AOT is
somehow representative of the amount of suspended aerosol in the optical path. It depends on the extinction (both
scattering and absorption) due to the suspended particles. Roughly, it is retrieved by the intensity of back-scattered light
as measured by the satellite (i.e. the largest the intensity the highest the optical thickness).
The SeaDAS software is not able to retrieve the atmospheric optical parameters in case of large amount of aerosol as in
correspondence of significant Saharan dust events, but just indicates the dust presence by the Aerosol Index (AI):
412
412
AI  100(log 10 ( M R490
)  (log 10 ( C R490
)).
where
M
2)
412
C 412
R490
and R490 stands for the ratio between the 412 and 490 radiances, measured and calculated (taking into
account the Rayleigh scattering only) respectively.
The passages relative to the Saharan dust events were re-processed using the new UM/RSMAS algorithm for ocean
colour atmospheric correction 8 and retrieval of aerosol types in order to test its capability to discriminate areas covered
by Saharan dust. The UM/RSMAS atmospheric correction algorithm uses a set of 18 models instead of the 12 used by
SeaDAS, and contains also two models that take into account the presence of dust in the atmosphere (Table 2).
Table 2
Model Number
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
18
Model Type
O O O O M M C C M M C C T T T T AfD AsD
Relative Humidity (%) 99 90 70 50 99 90 99 90 70 50 70 50 99 90 70 50 O=Oceanic; M=Maritime; C=Coastal; T=Tropospheric; AfD=African Dust; AsD=Asian Dust
3.2 The SKIRON/Eta limited area forecasting system
The SKIRON/Eta limited area forecasting system has been developed at the University of Athens at the framework of
SKIRON and MEDUSE projects 9,10. It is based on the Eta model 9. The system has a module able to describe the dust
cycle in the atmosphere 10. Further development of the system was made at the framework of the POSEIDON project
with emphasis in marine applications 11.
The dynamics of the Eta model is based on large-scale numerical solutions controlled by conservation of integral
properties, energetically consistent time-difference splitting technique and the step-like mountain representation. The
second-order non-linear advection scheme is designed for the horizontal advection terms in the model equations and for
passive substances (including dust concentration) a conservative positive definite scheme has been applied. The physical
package of the model represents atmospheric processes, which have smaller scales than the model grid structure and
therefore are not resolved explicitly but by utilizing several sophisticated parameterization schemes. The model physics
consists of the viscous sub-layer model with different approaches over water surfaces and over ground; the surface layer
scheme based on the Monin-Obukhov similarity theory; a turbulence closure scheme based on Kolmogorov-Heisenberg
theory; a non-linear fourth-order lateral diffusion scheme, with the diffusion coefficient depending on the deformation
and the turbulent kinetic energy controlling the level of small-scale noise; the Betts-Miller-Janjic deep and shallow
cumulus convection scheme; the large-scale condensation scheme; the Geophysical Fluid Dynamics Laboratory (GFDL)
radiation scheme and the land surface scheme of the Oregon State University with extensive modifications and a
corresponding set of high resolution ground conditions (soil and vegetation types, topography, SST). A detailed
description of the model dynamics and physics can be found in Nickovic et al. 12.
The dust modules of the SKIRON/Eta system incorporate the state of the art parameterizations of all the major phases of
the desert dust life such as production, diffusion, advection and deposition, dry and wet 13. These modules also include
the effects of the particle size distribution in order to simulate more accurately the size-dependent processes. Currently,
four particle size classes, determined by clay, small silt, large silt and sand, are in use. The desert dust cycle is described
by a set of K independent Euler-type concentration equations:
C k
C
C
C
  C 
 u k  v k  w  v gk  k  K H C k    K Z k 
t
x
y
z
z 
z 
 C 
 C 
 k 
 k 
 t  SOURCE  t  SINK
(3)
k  1, ..., K
allowing no inter-particle interactions. Here, K indicates the number of particle size classes. Ck is the dust concentration
of a k-th particle size class, u and v are the horizontal velocity components, w is the vertical velocity, v gk is the
 is the horizontal nabla operator, K H is the lateral diffusion coefficient, K Z is the
 C k 
 C k 
turbulence exchange coefficient, 
is the source, and 
is the sink term which includes both wet


 t  SOURCE
 t  SINK
gravitational settling velocity,
and dry deposition fractions. The total concentration C is a weighted sum of concentrations of K particle size classes:
K
C    k Ck ;
k 1
K

k 1
k
1
where
k
denotes the dust productivity factor of the k-th particle class. The SKIRON/Eta system with the modules
describing the dust cycle can run on hindcasting or forecasting mode.
4. EVENTS DETECTION AND ANALYSIS
On the basis of a visual inspection of SeaWiFS true color images and aerosol optical thickness at 865 nm (AOT), 93 dust
events occurring over the Mediterranean Sea in 2000 have been identified. In Table 3 these events are highlighted in a
calendar chart using different colors for different areas interested by the phenomenon with reference to our subjective
division of the Mediterranean basin. Regional sea name abbreviations are also used for a better localization of the events.
The table shows a marked seasonality of the Saharan outbreaks occurrence with their maximal frequency in March-April
and July-August. Furthermore it is evident that dust load does not cover homogeneously the entire Mediterranean but
only sporadic events (19 against 48 for the central and western, respectively) are observed in the eastern basin while the
central and western parts are strongly affected by the phenomenon.
The baric field pattern over the Mediterranean drives Saharan outbreaks toward the central basin in spring and toward the
western part in summer. Moreover it is interesting to note that in the central-western part of the basin the events show a
typical duration of 3-4 days.
Moreover each desert dust event has been analysed trough the observation of three different products:



True color satellite (SeaDAS processing)
Aerosol Optical Thickness @ 865 nm (UM/RSMAS processing)
SeaWiFS Atmospheric Correction Dust Models (UM/RSMAS processing)
A qualitative comparison between the true color and the dust model distribution images shows an overall good
agreement in selecting the desert dust models in the areas where the true color images seems to indicate their presence.
On the other hand, in order to process the dust-contaminated pixels, the new algorithm must allow for relatively large
radiance values as candidate cloud-free pixels. In reality, optically thick dust layers produce similar top of the
atmosphere radiance to relatively thin clouds. Note also that while for the AOT product some of those problems are
corrected, the aerosol model maps report all the pixels over the sea surface, indicating that, compared to the SeaDAS
processing, the UM/RSMAS processing treats all pixels and applies a post-processing to filter out the low quality pixels.
Figures 1 (a, b, c) show a radial dust plume spreading from the east Libyan coast over the Ionian and Levantine Sea. The
model map correctly discriminate the dusty pixels from the cloudy ones. The AOT image reaches its maximum value in
correspondence of the dust plume leaving the eastern tip of Libya in which, from the true colour image, it is difficult to
discriminate the suspended sand from the land. Comparing the model and AOT maps it is evident that the automatic
post-processing masking is applied only to AOT. This image represents one of the most intense event occurred in the
eastern Mediterranean and has been used to investigated the effect of dust deposition on the phytoplankton biomass (see
paragraph 5).
Analysis of the previous images has shown that the UM/RSMAS processing correctly detects the presence of Saharan
dust in the atmosphere. This enables mapping of the dust model occurrence to highlight the Saharan dust load spatial
distribution. The latter can in turn provide an indication of areas of enhanced deposition probability. Since the intensity
of the phenomenon and the occurrence of precipitation were not taken into account, this map should be more
representative of the two-dimensional dry deposition pattern than the total (wet+dry).
According to Table 3 the frequency map of the dust model occurrence (Figure 2) shows that the preferential pathways
through which the dust plumes move northward are located over the central and the western part of the Mediterranean
basin. Furthermore this map displays the absolute minimum value in the Gulf of Lion and a wide zone of relatively low
values in the Levantine basin.
Table 3. Calendar chart of the Saharan dust events occurred in 2000. Events have been chosen by visual inspection of SeaWiFS true color images as well as by deposimeter
measurements in Corsica.
Figure 2a. True color SeaWiFS image for the dust event over the eastern Mediterranean (April 10, 2000).
Figure 2b. Aerosol optical thickness relative to the dust event of April 10, 2000.
Figure 2c. Aerosol model distribution relative to the dust event of April 10, 2000.
Figure 3. Frequency map of the dust model occurrence over the Mediterranean Sea for the year 2000. This product has been evaluated
considering all the cases mentioned in Table 2
5. SAHARAN DUST FERTILIZATION AND CHLOROPHYLL FIELD RESPONSE
Chlorophyll_a [mg m-3]
Two selected Saharan dust events seen in the SeaWiFS true color images have been analyzed in order to establish the
possible effect of dust deposition on the chlorophyll field. SeaWiFS imagery allows locating the area covered by the dust
load both visually from the true color images and by the model type selected during the atmospheric correction phase.
Time [Julian Day]
Fig.4 Time series of chlorophyll spatial average for the selected region around Crete (inset) in April 2000. Bars indicate standard
deviation around each value. Numbers indicate percent of good pixels in the box used to compute each spatial average.
The qualitative comparison between SeaWiFS results and the SKIRON atmospheric model output indicates a very close
correspondence between model and SeaWiFS aerosol load. Keeping in mind that precipitation is the principal deposition
mechanism, we selected the areas under aerosol load for which the model predicted wet deposition. A box of variable
Chlorophyll_a [mg m-3]
dimensions (Figs. 4, 5 and 6 inset) was chosen for each event within the wet deposition areas and a time series of
spatially averaged chlorophyll was computed for a time window of 5 days before and 10 days after the event (Figs. 4, 5
and 6). Bars in the time series indicate standard deviation of the chlorophyll values and numbers indicate the percent of
good pixels in the box.
Time [Julian Day]
Fig. 5.Time series of chlorophyll spatial average for the selected region south of Crete (inset) in April 2000. Bars indicate standard
deviation around each value. Numbers indicate percent of good pixels in the box used to compute each spatial average.
Fig, 6a. Saharan dust event over the Tyrrhenian and Ionian Seas, July 18 2000.
In the already mentioned figures examples of this analysis are shown. The true colour image (fig. 2a) shows the
occurrence of a dust event over the Eastern Mediterranean around April 9 2000, which fully developed the next day and
persisted until April 12 2000. On the basis of this image, a box in the Levantine Sea around Crete (Fig 4 inset) was
selected and the time series of the averaged chlorophyll content was computed (Fig. 4). A rise in chlorophyll from 0.2 to
0.4 mg/m3 is evident starting on April 9 (Julian day 100 in Fig. 4) lasting 5 days. A smaller box south of Crete was also
chosen, relatively to the same event, representing the area of maximum model wet deposition (Fig. 5 inset). The trend in
Chlorophyll_a [mg m-3]
chlorophyll is much more pronounced (0.2 to 0.6 mg/m 3), with the same fast response even though the maximum value
is attained later.
The response in the Western Mediterranean seems to be slower: a dust event initiated on July 17 and fully developed on
the next day (Figs 6) is accompanied by a rise in chlorophyll (Fig. 6b) in the selected box located between Sicily,
Sardinia and Tunisia. It is interesting to note that there is a 2-3 days time lag between the dust event and this rise in
chlorophyll. Also, the duration of the higher chlorophyll phase is brief. Another dust event occurred in the same area on
July 24-26 could be possibly responsible for the second rise seen in figure 6b, which starts at day 205-206.
Time [Julian Day]
Fig 6b Time series of chlorophyll spatial average for the selected region between Sicily, Sardinia and Tunisia (inset). Bars indicate
standard deviation around each value. Numbers indicate percent of good pixels in the box used to compute each spatial average.
6. CONCLUSIONS
In this paper SeaWiFS data were used to monitor the occurrences of Saharan dust events over the Mediterranean and to
investigate the atmospheric deposition impact on the biological activity. The analysis shows that 93 events occurred in
the Mediterranean in the year 2000 with a strong seasonal behaviour. Contrary to previous studies we observed that
principal pathway of the dust transport is toward the western Mediterranean Sea and only few events affect the eastern
basin. The present study highlights that the new algorithm UM/RSMAS for the “atmospheric correction” well detects
the African dust presence by comparison with true colour images. Furthermore, SKIRON dust load maps show a
qualitatively good agreement with the satellite retrieved products. The analysis of the chlorophyll time series in
correspondence to the dust wet deposition showed that the atmospheric contribution seems to have some influence on the
productivity of the Mediterranean basin. Although biological dynamic is meanly driven by the circulation features of the
basin (Figs 1), results show that the atmospheric nutrient deposition gives some evident signal in enhancing biological
activity. An more exhaustive analysis (using the model outputs) to the whole dataset is planned for the next future to
infer on the total annual amount of atmospheric nutrients that reach the sea surface. This will obviously help to
understand to which extent the Saharan desert play a role in the biogeochemistry of the basin.
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