In the present work, atmospheric mineral dust from a MACC-II short
reanalysis run for 2 years (2007–2008) has been evaluated over
northern Africa and the Middle East using satellite aerosol products
(from MISR, MODIS and OMI satellite sensors), ground-based AERONET
data, in situ PM
Mineral dust has significant impacts in many regions of the world. Airborne
mineral dust can have numerous repercussions on human health, such us
allergies, respiratory diseases and eye infections (WHO, 2006; Giannadaki et al., 2014); it is also
linked to epidemics of deadly meningitis in the region known as the
meningitis belt (Sultan, 2005; Thomson et al., 2006; Pérez
García-Pando et al., 2014). Increased airborne mineral dust reduces
visibility (Wang et al., 2008) with consequent problems in road and air
transportation, while dust storms have negative impacts on agriculture
causing loss of crop and livestock (Stefanski and Sivakumar, 2009). Desert
dust deposition also influences the biogeochemical cycles of both oceanic and
terrestrial ecosystems (Okin et al., 2004; Jickells et al., 2005; Mahowald
et al., 2005, 2010; Schulz et al., 2012) via for example the release of iron
from dust into seawater (Nickovic et al., 2013). Indeed, due to the many connections with the Earth's
systems, mineral dust can also impact the carbon cycle and atmospheric
Mineral dust also has a significant impact on the Earth radiative budget (IPCC, 2013), through both direct and indirect effects. The radiative forcing resulting from large changes in the global dust cycle is thought to have played an important role in amplifying past climate changes (Jansen et al., 2007; Abbot and Halevy, 2010). Indirect effects of dust on cloud formation and precipitation rate can provide additional changes in the Earth's radiation balance and hydrological cycle. Several studies have observed that mineral dust generates large concentrations of cloud condensation nuclei (CCN) and ice nuclei (IN) (Vali, 1985; Klein et al., 2010; Hoose and Möhler, 2012). Dust particles acting both as CCN and IN modify the cloud microphysical and macrophysical properties, namely droplet size, cloud albedo, cloud cover, vertical extent and lifetime (Hansen et al., 1997; Heymsfield et al., 2009; Cziczo et al., 2013).
The Sahara and its margins contribute to more than half of the global dust emissions (Huneeus et al., 2011; IPCC, 2013). Airborne African dust has a complex relationship with climate, its transport being strongly controlled in turn by climate variability (Prospero and Nees, 1986; Moulin et al., 1997; Ginoux et al., 2004; Mahowald et al., 2010; Alonso-Pérez et al., 2011; Rodríguez et al., 2014) and changes in the land surface conditions (Middleton and Goudie, 2001; Moulin and Chiapello, 2004). These climatological studies may be further extended with new and improved model simulations for long-term periods.
Dust modelling is essential, not only to have a powerful tool to
predict the global or regional dust budget and its interactions in the
climate–weather system, but also to complement remote sensing and
in situ observations and to understand the processes involved in the
dust cycle. Several experimental and operational dust forecast systems
have been developed in the recent years, including global models such as
the Navy Aerosol Analysis and Prediction System (NAAPS; Westphal
et al., 2009), the interactive chemistry and aerosol model (INCA/LMDz;
Hauglustaine et al., 2004) and the aerosol model at the European
Centre for Medium-range Weather Forecasts (MACC-ECMWF; Morcrette
et al., 2009; Benedetti et al., 2009); and European regional models
such as BSC-DREAM8b (Nickovic et al., 2001; Pérez et al., 2006a,
b; Basart et al., 2012), CHIMERE (Menut, 2008; Schmechtig et al.,
2011), and NMMB/BSC-Dust (Pérez et al., 2011; Haustein et al.,
2012). These models are participating in the World Meteorological
Organization (WMO) Sand and Dust Storm Warning Advisory and Assessment
System (SDS-WAS), Regional Center for Northern Africa, the Middle East and
Europe (
Studies comparing and evaluating the temporal (on annual, seasonal and daily basis) and spatial variability of desert dust load and deposition simulated by different models, contribute to determine the degree of uncertainty in estimates of dust emission and transport. They highlight the sources of uncertainty in these estimates, and point to the key foci for future research in order to constrain them (e.g. Tegen, 2003; Kinne et al., 2006; Textor et al., 2006; Huneeus et al., 2011). Dust-related products, such as horizontal visibility, particulate matter concentration, aerosol optical depth (AOD), and extinction vertical profiles will likely be incorporated as added value information in future climate services databases. Long-term dust-related observations and model reanalysis may contribute to understand assessments and plan activities of health and energy communities and to other economic sectors in many regions of the world. For example, comprehensive long-term dust records might help to understand and prevent health problems through epidemiological studies. Dust climatologies might be used to perform feasibility studies of future solar power plants in arid and desert regions.
The present study evaluates and analyses the MACC-II (Monitoring Atmospheric Composition and Climate; Interim Implementation) reanalysis dust simulation for the period 2007–2008 over northern Africa, the Middle East and adjacent regions using ground-based and satellite observations. We clarify that this is not the 10-year MACC reanalysis that is publicly available for the period 2003–2012, but a reanalysis of 2 years implemented specifically for this study. The new MACC-II reanalysis incorporates an improved dust parameterization scheme. Some evaluations from the atmospheric composition MACC reanalysis have been published (e.g. Elguindi et al., 2010; Bellouin et al., 2013; Inness et al., 2013; Cesnulyte et al., 2014), but none of these studies focused specifically on mineral dust products. An important objective of the MACC-II reanalysis evaluation is to examine its ability to reproduce aerosol spatiotemporal variability.
The description of the MACC-II reanalysis is provided in Sect. 2, while Sect. 3 includes the description of the different observational data sets used for the model evaluation. The results of the comparison are shown in Sect. 4. Finally, Sect. 5 summarizes the most important findings of the present study.
Starting in 2008, ECMWF has been providing daily aerosol forecasts including dust as part of the EU-funded projects GMES (Global Monitoring for Environment and Security, now COPERNICUS), MACC and MACC-II. A 10-year reanalysis for 2003–2012 has also been completed during the MACC-II project (Inness et al., 2013). A detailed description of the initial implementation of the aerosol modules is given in Morcrette et al. (2009) for the modelling part, and in Benedetti et al. (2009) for the assimilation part. The physical parameterizations for the aerosol processes are modelled after the LOA/LMD-Z model (Boucher et al., 2002; Reddy et al., 2005). However, some modifications to the original schemes were introduced over the years. Some of these modifications are described in Morcrette et al. (2011).
Five types of tropospheric aerosols are considered in the model:
sea-salt, dust, organic and black carbon, and sulphate
aerosols. Prognostic aerosols of natural origin, such as mineral dust
and sea-salt are described using three size bins. For dust, bin limits
are at 0.03, 0.55, 0.9, and 20 microns while for sea-salt bin limits
are at 0.03, 0.5, 5 and 20 microns. Emissions of dust depend on the
10
Several types of removal processes are considered: dry deposition including the turbulent transfer to the surface, the gravitational settling, and wet deposition including rainout by large-scale and convective precipitation and washout of aerosol particles in and below the clouds. The wet and dry deposition schemes are standard, whereas the sedimentation of aerosols follows closely what was introduced by Tompkins (2005) for the sedimentation of ice particles. Hygroscopic effects are also considered for organic matter and black carbon aerosols.
MODIS Dark Target AOD Collection 5 data at 550
Accumulated daily dust emissions from MACC-II for winter
(JFM), spring (AMJ), summer (JAS) and autumn (OND) 2007
(
Following positive changes in the dust parameterization scheme, it was
proposed to run a short MACC-II reanalysis for 2 years (2007–2008)
with a more recent model version. No additional dust-specific
observations were used in this new reanalysis, but only MODIS Dark
Target AOD at 550 a revision of the dust emission potential for the Sahara–Sahel
region, now divided in four sub-regions, as opposed to the single
region in the previous version (indicated in Fig. 1) a retuning of the dust emissions a bug-fix for the wet deposition meteorological model changes, including modifications to the
cloud scheme with the introduction of prognostic rain and snow
variables, improvements to the convection scheme, and retuning of
other physical processes parameterizations (orographic gravity wave
drag, diffusion, surface roughness, etc.), assimilation changes including snow analysis, improved all-sky
microwave assimilation, and assimilation of precipitation from
ground-based radar.
It is difficult to quantify the contribution of the individual changes to
the differences in the aerosol forecast, but it is fair to say that possibly the
biggest impact is that of the dust parameterization and to some extent the
changes in the cloud scheme.
The MACC-II reanalysis runs at T255L60, which is approximately
In this study we have used MACC-II AOD and dust aerosol optical depth
(DOD) at 550
AOD at 440 and 670
Observations of different types of aerosols and mineral dust, available in the study area, are used in this work. It should be noted that the most important ground-based observations are those performed in the vicinity of dust source regions such as the Sahara or the Middle East, which are very sparse. Therefore, aerosols measurements from satellites have been also analyzed in this study. A summary of the most important features of the observations used to validate estimated aerosols and mineral dust from MACC-II are described below.
AOD and AE are obtained from the AErosol RObotic NETwork (AERONET,
Localization of the AERONET stations, grouped by regions,
lidar sites and
Name and location of the AERONET stations. Cloud screened and calibration quality-assured AERONET level 2.0 data has been extracted for each station.
Stations were selected based on their location and data
availability for the study period (from January 2007 to
December 2008). A total of 26 AERONET stations were analyzed
and grouped into eight regions with geographically distinct
characteristics: Sahara, Sahel, north-western Maghreb, subtropical
North Atlantic, western Mediterranean, central Mediterranean, eastern
Mediterranean, and the Middle East (Fig. 2 and Table
Cloud-screened and calibration quality-assured AERONET Level 2.0 data
(Holben et al., 1998) has been extracted for each station. Since
MACC-II provides AOD at 550
The vertical distribution of the total and natural (sea salt and
mineral dust) extinction coefficient from the MACC-II 2007–2008
reanalysis has been validated using ground-based lidar
observations. Only two operational lidars have provided extinction
vertical profile data under almost pure mineral dust conditions, with
climatological significance, within our study area during the period
from January 2007 to December 2008: (1) at M'Bour (Dakar, Senegal,
14.4
The CE370-CAML lidar located at M'Bour, near Dakar (Senegal), is an
eye-safe system that comprises a Nd-Yag II laser which emits laser
pulses at 532
In both lidar stations, the raw data profiles have been range corrected
(Campbell et al., 2002), and the overlap correction function has been
applied using the slope method (Kunz and de Leeuw, 1993). The lidar
signal in both stations can be used, for heights greater than
Particulates with an aerodynamic diameter less than 10
Although there are other
We have used the
The AOD spatial distributions obtained from satellites provide unique information to assess the spatiotemporal distributions of AOD simulated by MACC-II. This is a particularly interesting point because models do not simulate aerosols with the same skill in different regions of the Earth, and satellite sensors do not show the same accuracy to measure aerosols in all regions because data inversion is affected by meteorological conditions, land surface properties, and the magnitude of the dust loading (Banks et al., 2013). We have also used the observed AOD data from satellites over AERONET stations for comparison with AOD from MACC-II, and with AERONET observations which are the reference, so we can properly assess the differences observed in the simulations.
AOD from satellite sensors and from the MACC-II reanalysis have been plotted in lat/lon maps. Satellite retrieved AODs for the pixels in which the ground stations are located are used.
The Multi-angle Imaging SpectroRadiometer (MISR) instrument, flying
aboard the NASA Earth Observing System's Terra satellite
(
MISR can retrieve aerosol properties (aerosol shape, size and single
scattering albedo) over bright desert areas due to its unique
capability of multi-wavelength observations at forward and backward
directions (Kahn et al., 2005, 2010). Further details about the
aerosol algorithm and its retrieval can be found in Diner
et al. (2001, 2008). According to Kahn et al. (2010),
In this work, daily Level-3 AOD data (MILDAE3)for the green channel
(555
The Ozone Monitoring Instrument (OMI) was launched in July 2004 on NASA's
EOS-Aura satellite. OMI provides aerosol information on a global scale at
a daily basis, passing over a certain location once or twice a day.
A detailed description of the characteristics of the OMI instrument is given
by Levelt et al. (2006). Two aerosol inversion schemes are available for OMI
measurements: the OMI near-UV (OMAERUV) and the multi-wavelength algorithm
(OMAERO). The OMAERUV algorithm uses the range of near UV region
(354–388
We used OMAERUV algorithm for retrieving aerosols over arid and semi-arid regions because the reflectance is small in the near-UV spectrum, whereas in the visible and near-IR these surfaces appear very bright and it is difficult to retrieve aerosols (Torres et al., 2007). However, aerosol content from OMI shows a dependence on the level height of the detected aerosol layer, and hence, dust plumes well mixed over the entire boundary layer or residual dust layers aloft may be overestimated (Ginoux and Torres, 2003).
The AURA/OMI Level 3 daily global
Moderate Resolution Imaging Spectrometer (MODIS) onboard the NASA EOS (Earth Observing System) Terra and Aqua satellites (Salomonson et al., 1989) provides aerosol properties over both land (Kaufman et al., 1997) and ocean (Tanré et al., 1997) with a near-daily global coverage.
The standard AOD product is retrieved using the dark-target approach (Kaufman
et al., 1997) at near-infrared wavelengths (2.1 and 3.8
The daily Level 3 aerosol products from Aqua (collection 5.1, MYD08)
at
Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is the
primary instrument on Cloud-Aerosol Lidar and Infrared Pathfinder
Satellite Observations (CALIPSO) satellite launched on April 2006 by
NASA. The CALIOP instrument, the first lidar with polarization
capabilities in the space, utilizes three receiver channels, one
measuring the 1064
In this study we used the extinction profiles at 532
An important objective of the MACC-II reanalysis evaluation is to
examine its ability to reproduce aerosol spatiotemporal
variability. In this section, we assess to what extent the MACC-II
system is able to capture details in the spatial distribution of
aerosols in our study area, and the seasonal and interannual
changes. Since transport over the Atlantic Ocean is generally well
observed by most satellite sensors, and well simulated by dust models,
we have focused our attention on inland areas in northern Africa and the Middle East, where larger differences are expected in AOD between
MACC-II and satellite observations. MISR is the most reliable of all
satellite observing systems used in this study since it has been
specially designed to measure with little uncertainty over high
reflective surfaces, although it has difficulties to retrieve the
magnitude of the largest dust events (Banks et al., 2013). For this
reason, and because MODIS dark-target AOD data at 550
Seasonal AOD averages from MACC-II, MISR, MODIS and OMI for the period 2007–2008. Winter (JFM), spring (AMJ), summer (JAS) and autumn (OND). AOD averages have been computed using only those common pixels of simultaneous days, for each MISR pass, for MACC-II, MODIS and OMI.
We have computed the seasonal spatial AOD averages of MACC-II, MODIS and OMI
matching the MISR observations to avoid bias due to differences in the
temporal and spatial sampling, selecting those common pixels of simultaneous
days, for each MISR pass, for MACC-II, MODIS and OMI. We have compared the
seasonal AOD averages from MACC-II (at 12 UTC) with AOD from MISR, MODIS and
OMI for the period 2007–2008 (see Fig. 3). The AOD seasonal averages of
MACC-II, MISR, MODIS and OMI, using all available data for each system in the
period 2007–2008, are shown in Fig. S1 in the Supplement. As expected, there are differences
with the AOD averages computed using only common MISR data, but even so, AOD
“climatologies” obtained from each satellite and from MACC-II show
similarly the main AOD patterns. MACC-II AE seasonal averages (2007–2008)
are shown in Fig. S2. We have computed the AOD MACC-II-MISR normalized mean bias (NMB) expressed as
100
MACC-II dust emissions show seasonal patterns (see Fig. 1). In northern Africa, the Bodélé depression in Chad is the area with the highest dust emissions, achieving a maximum during winter and autumn months. The winter dust activity is maximum in low latitudes, and as the year progresses, dust activity shifts to higher latitudes. The emission activity is driven by the latitudinal shift of the Intertropical Front which corresponds to the convergence zone between the dry northern winds, called the Harmattan, and the humid monsoon winds from the south. During spring and summer, the dust activity is at its maximum and the dust transport shifts to northern latitudes. Over much of the Arabian Peninsula, the main dust sources extend in a continuous band from the northern part of the Tigris–Euphrates basin to the coast of Oman. Dust activity increases strongly in spring and summer, and weakens in winter and autumn.
The seasonal AOD fields from MACC-II also show a distinct seasonal
pattern linked the spatial distribution of dust emissions throughout
the year (Fig. 3). Winter is characterized by showing low AOD values
in most regions of our geographic domain (Fig. 3a–d),
except in the Gulf of Guinea. The similarity between MACC-II and MISR
is noteworthy. The AOD MACC-II-MISR NMB ranges from
In spring a considerable increase in dust activity in the North Sahel,
the Sahara and the Arabian Peninsula, and a considerable reduction of
AOD in the Gulf of Guinea is observed (Fig. 3e–h). This
corresponds with significantly lower MACC-II AE values over the Sahara
and the Arabian Peninsula, and higher MACC-II AE over the Gulf of
Guinea (Fig. S2b). The most remarkable feature is that MACC-II
underestimates over the Bodélé, and overestimates in North
Sahara. Increased AOD over the Arabian Peninsula and the Iraq–Persian
Gulf corridor is simulated by MACC-II with AOD values close to those
observed by MISR. The MACC-II-MISR NMB values fall within
In summer, we observe a significant reduction in AOD in northeast
Africa, the eastern Mediterranean basin, and the Sahel, compared with
spring, which corresponds to significantly higher MACC-II AE values
(
Finally in autumn, the lowest annual AOD values are observed by
satellites and simulated by MACC-II (Fig. 3m–p). In
contrast to winter, in this season high AOD values are not observed
over the Gulf of Guinea, only on the coast line. The unique hot-spot
which is well observed by the three satellite sensors is the
Bodélé depression, with relatively high values of AOD, which
is underestimated by MACC-II. In this area, however, MACC-II simulates
the lowest AE values (
The agreement between MISR, OMI, MODIS and MACC-II is, in general, very good, reproducing the same AOD patterns in the four seasons (Fig. 3). In this MACC-II evaluation, it is important to note that over desert regions such as North Africa and the Middle East, the AOD values from MISR and MODIS (fundamentally AOD DB) products have differences of around 0.1 to 0.3 (Shi et al., 2011).
Particular spatial discrepancies include the low AOD values simulated
by MACC-II in the Bodélé. According to Schmechtig
et al. (2011), the observed surface wind velocities in the
Bodélé depression are as high as 20
A major disagreement between MISR and MODIS is found in the Arabian Peninsula in spring (Fig. 3f and g) and summer (Fig. 3j and k). This had been reported previously by Shi et al. (2011) who found that one of the regions of the world where the MISR retrievals are much greater than those from the MODIS is the Arabian Peninsula. From the comparison between MISR and MODIS with AERONET observation site at Solar Village (Saudi Arabia), performed by Shi et al. (2011), we can conclude that at this site there is better AERONET/MISR agreement than AERONET/MODIS agreement. Also MACC-II agrees better with MISR than with MODIS.
Large differences observed between satellite and modelled values are
also linked to the coarser spatial resolution used in the MACC-II
reanalysis (
Interannual percentage variations of AOD (2008 minus 2007) from MACC-II, MODIS-Aqua and MISR for winter (JFM), spring (AMJ), summer (JAS).
At present it is not possible to know with sufficient detail the causes behind the interannual variability of mineral dust (Rodríguez et al., 2014, and references herein). However, it is known that much of the year-to-year dust variability is modulated by changes in large-scale atmospheric circulation patterns as well as land surface conditions. In this section, we assess how MACC-II is able to capture the interannual variations of AOD, by comparing with interannual variations recorded by satellites. We calculated the percentage differences in AOD between 2007 and 2008 observed by MISR, MODIS and OMI, and simulated by MACC-II for each month and for the four seasons (see Fig. 4), using only simultaneous AOD observations/simulations with MISR. For the sake of brevity, we only show the results of the interannual differences corresponding to winter, spring and summer, since in autumn the interannual variations were very small.
In winter, MISR and MODIS show decreases in AOD (15–50 %) from 2007 to 2008 in western Africa and the Sahel, in the eastern Mediterranean, and south of the Arabian Peninsula, and AOD increases, (10 and 20 %) in eastern Syria, Iraq and the northern Persian Gulf, as well as in the northern half of the Red Sea (Fig. 4b and c). MACC-II captures quite well all these AOD interannual changes in these regions (Fig. 4a). Qualitatively, the patterns of the AOD differences observed by MISR and MODIS and simulated by MACC-II are quite similar.
In spring, MISR and MODIS observe lower values (5–20 %) in 2008 compared to 2007 (Fig. 4e and f) in much of the Sahel, in a band stretching from the Sahel to the eastern Mediterranean across south eastern Libya and Egypt, and in the central and western Mediterranean. These differences are well simulated by MACC-II (Fig. 4d). Regarding increases in AOD, these are similarly observed (5–20 %) by both MISR and MODIS over the western part of the Sahara (Algeria, Mauritania and southern Morocco). The satellites also recorded increases in the Gulf of Guinea (10–15 %), in Turkmenistan (10–20 %), especially by MODIS, the southern half of the Red Sea, and in a wide and long corridor that goes from Iraq to the Arabian Sea (15–25 %). All AOD increases are correctly simulated by MACC-II except the significant increase over Iraq, which is clearly underestimated (0–5 %) by MACC-II.
In summer, in general, the AOD interannual changes simulated by MACC-II (Fig. 4g) agree much better with the AOD changes observed by MISR (Fig. 4h) than with those detected by MODIS (Fig. 4i). The AOD decreases (10–20 %) registered by MISR and MODIS on the western centre of the Sahara (Algeria and the northern half of Niger) are well simulated by MACC-II. However, reductions in AOD observed by MODIS in the eastern Sahara are not detected by MISR nor simulated by MACC-II. MODIS shows a very strong AOD increase in 2008 compared to 2007 over the Iraq–Oman corridor with very high values (20–50 %) in Iraq and the Persian Gulf, which are also recorded by MISR. These values, despite being well simulated by MACC-II, appear to be smoothed and less intense. MODIS observes strong AOD increases (15–25 %) over southern Iran, Turkmenistan and Azerbaijan, which are not observed by MISR nor simulated by MAAC-II. MACC-II simulates, with smoothed values, increased AOD in the southern half of the Red Sea, recorded by MODIS and MISR, and moderate increases (10–15 %) between Chad and Sudan that are registered by both MISR and MODIS.
In summary, MACC-II is able to correctly simulate the interannual variations of AOD for the 2-year period 2007–2008 in each season showing a better agreement with MISR than with MODIS. The dust corridor from Iraq to Oman, covering the entire Persian Gulf, is the region of our study domain in which MACC-II and satellite sensors show the greatest AOD interannual changes, probably because in Syria and Iraq (Mesopotamian region), soil conditions are closely linked to interannual changes in water availability. It is in this region where MACC-II has greater difficulties correctly simulating the interannual changes.
Seasonal averages of AODcoarse (DOD) from AERONET
Monthly averages (2007–2008) of AOD from AERONET, MACC-II,
MODIS-Aqua and OMI, AODcoarse (DOD) from AERONET and MACC-II, and AE
from AERONET and MACC-II over Tamanrasset (
Taylor diagrams where seasonal AOD values from MACC-II,
MODIS-Aqua and OMI are compared with AERONET AOD, used as reference,
for Sahara
In this section, we evaluate the ability of the MACC-II model to reproduce the dust cycle in our study region. MACC-II AOD is quantitatively evaluated by means of the comparison against AERONET and satellite data (MODIS and OMI) in different geographic regions using AERONET as the reference (see Fig. 2). MISR is discarded here due to its low temporal resolution (see Sect. 3.2.1) in comparison with MODIS and OMI.
For this comparison, AOD and AE outputs from MACC-II at 06, 09, 12,
15
and 18 UTC have been evaluated with near AOD and AE from AERONET
observations averaged for these hours (
A set of standard skill scores defined within the MACC-II project (see
Appendix A) have been computed using data from those days of the period
2007–2008 when there are simultaneous data of MACC, OMI, MODIS and AERONET.
They have been computed on a monthly and seasonal basis for each AERONET
station and for the eight sub-regions defined in Fig. 2 and Table
Dust content is difficult to verify because bulk optical observations
are not specific for dust. Since AOD is the degree to which a mixture
of atmospheric aerosols prevents the transmission of light by
absorption or scattering, a criteria is needed for filtering data to
ensure that most of the AOD is influenced by mineral dust, i.e. the
dust optical depth (DOD). Nevertheless, the criteria should not be as
restrictive as to reduce drastically the number of observations
because it would preclude proper assessments of this evaluation in
dust transport regions where mineral dust concentrations are
significantly lower than in near-source regions. For the present
AERONET comparison, we used
Skill scores quantifying the level of agreement between MACC-II and AERONET AE and DOD (obtained from direct-sun and AODcoarse from the SDA retrievals) daily means, obtained by regions.
By using the criterion of AODcoarse from the SDA retrieval, while the
number of paired data points in the MACC-II-AERONET evaluation
experienced no significant changes in the Sahara and the Sahel, this
number grew significantly in other regions, especially in the dust
transport corridors such as the Mediterranean regions, the North-western Maghreb and the subtropical North Atlantic. In long-range
transport areas (i.e. North Atlantic and the Mediterranean), the AE
filter applied to direct-sun AOD observations (
The modified normalized mean bias (MNMB) for AODcoarse from SDA
retrieval showed varying results, improving in some regions and
worsening in others in comparison with direct-sun DOD
observations. The mean bias (MB) improves in all regions in comparison
with the direct-sun DOD AERONET observations, except in the Sahara. In
this sense, it is worthy to mention that AODcoarse considers
super-micron aerosols. Meanwhile, the model takes into account all their
bins, including sub-micron particles. Concerning the correlation
coefficient, it increases significantly in all regions, except in the
Sahel and central Mediterranean. We confirmed that the approach to
estimate DOD using AOD coarse from the SDA retrievals is quite
reasonable because the seasonal averaged MACC-II AE is
On an annual basis, the root mean square error (RMSE) varies between
0.27 in the Sahel to 0.06 in the subtropical North Atlantic
(Table
In the next sections, a detailed analysis by regions is presented. For the sake of brevity we only show the individual results of the MACC-II-AERONET comparison in six stations considered representative of the most characteristic regions we identified in our study domain (Fig. 6). MACC-II and AERONET daily DOD means records for the period 2007–2008, at these six stations, are available in Fig. S4.
Tamanrasset is a station in the centre of the Sahara desert (southern Algeria).
A detailed characterization of the AERONET Tamanrasset station can be found
in Guirado et al. (2014). Both AOD and DOD annual variation is well captured
by MACC-II (Fig. 6a and b) but some overestimation is found in June, July and
October in comparison with AERONET observations. The comparison of DOD daily
values from MACC-II and AERONET demonstrates the summer overestimation in
more detail (Fig. S4a). However, the daily comparison also highlights some
very high DOD values recorded by AERONET that are not well captured by
MACC-II. These events are normally observed in summer associated with
mesoscale convective processes (Tegen et al., 2013) south of Tamanrasset and
driven by the monsoon. Despite these features, the summer MACC-II-AERONET
correlation coefficient (
The best correlation (0.93) and the lowest fractional gross error (FGE) in DOD (0.64) is found in April, while the lowest correlation (0.05) and the highest FGE (1.03) correspond to January because in this month the AERONET DOD is extremely low (0.03).
A significant finding is the large underestimation of MACC-II AE,
observed all year, but especially from June to January (Fig. 6c). This
clear bias might be partially explained by the re-balanced dust
emissions scheme used in this MACC-II reanalysis which produces
coarser dust particles by introducing dust mass in relation 0.5, 2, 4
into the fine, medium and coarse dust bins, respectively. Moreover,
missing local anthropogenic sources in North Africa (e.g. Liousse
et al., 2010; Rodriguez et al., 2011) not considered in the model can
affect these lower MACC-II AE values. High AE values found in December
and January (
Compared with satellites, we observe a good agreement between MACC-II and MODIS (Fig. 7a), except in summer, although the agreement between MACC-II and AERONET is worse than the agreement MODIS-AERONET. MACC-II behaves better than OMI, which significantly overestimates AOD throughout the year (Fig. 7a).
In the Sahel we focus on the results at the Banizoumbou station (Niger), the station located in the innermost part of the “Sahelian Dust Transect” (Marticorena et al., 2011). The agreement in monthly AOD averages between MACC-II and AERONET is better than the agreement between MODIS and AERONET, for most of the months, and similar to that of OMI (Fig. 6d). In this station, we had the opportunity to compare with other model validation analysis. The daily AOD correlation between MACC-II and AERONET for the period 2007–2008 at Banizoumbou is 0.62, while the correlation between the regional CHIMERE model and AERONET at this station reported by Schmechtig et al. (2011) for 2006 is 0.44.
The DOD month-to-month variability is satisfactorily tracked by
MACC-II (Fig. 6e). The best correlation is found in January and
February (0.77 and 0.81, respectively), with a relatively low FGE
(0.52 and 0.40, respectively). During these months strong Harmattan
winds transport dust from the Sahara. The maximum DOD is observed in
April, just before the wet season driven by the monsoon. During the
rainy season a slight underestimation of MACC-II DOD is observed,
likely related with dust emitted by wet mesoscale convective events
(Marticorena et al., 2010) associated with the monsoon, which are not
well reproduced by MACC-II (see Fig. S4b). The lowest correlation is
recorded in August (0.20) and September (0.26), months in which we
found slightly higher FGE (0.58 and 0.45, respectively). These
results agree with the fact that MACC-II dust emissions are negligible
across the Sahel (Fig. 1c) in summer. The yearly course of AE is well
captured by MACC-II (Fig. 6f). However a clear underestimation is
observed from September to February. This might be a fingerprint of
the re-balanced dust emissions scheme in MACC-II with too many coarse
particles in source regions. This is a period driven by the Harmattan
winds carrying dust from the Sahara in a relatively short path in
which there is hardly time for coarse particles deposition. When the
Sahelian stations are grouped, we find a moderate correlation between
MACC-II and AERONET daily DOD values (0.55), the same as for the
Sahara (Tamanrasset), but with a number of data 4 times higher
(Table
In the Middle East, Solar Village (Saudi Arabia), located in the centre of
the Arabian Peninsula, is a long-term high quality AERONET
station. The AOD annual course from MACC-II is in good agreement with
AERONET and MODIS (Fig. 6g), but a clear overestimation is observed
from April to September, period when the maximum monthly AOD is
observed with a peak in April–May. OMI overestimates AOD more than
MACC, and it does throughout the whole year. MACC-II AOD
overestimation is significantly reduced in case of DOD and is
restricted to the period July–October (Fig. 6h). Solar Village shows
a rather broad range of correlation coefficients, with minimum in
December (0.38) and maximum in October (0.82). The FGE ranges from
0.36 in April and May to 0.84 in October (see also daily DOD records
in Fig. S4c). The period July–October partially coincides with the
southwest monsoon, occurring from June to September and with the
autumn transition covering the period October–November. Middleton
(1986) and Smirnov et al. (2002) reported that the dust haze
experienced in the Arabian Peninsula from June to August is related to
a large-scale dust flow originated by the southwest monsoon
circulation. As pointed out by Cesnulyte et al. (2014), when
validating a previous MACC-II reanalysis, the AOD overestimation
during the southwest monsoon period is likely related to a poor
representation of rain and aerosol removal processes in MACC-II. When
the nine AERONET stations in the Middle East are grouped we find
a better correlation between MACC-II and AERONET (0.71), than for the
Sahara and the Sahel (0.54), and, in general, better skill scores than
in these latter regions (Table
Concerning AE, MACC-II reproduces fairly well the month-to-month
variation, matching the AERONET AE values during the first half of the
year (January–July), but failing in the period August–December when
a notable underestimation (
In the eastern Mediterranean, Sede Boker station (Israel) shows much lower
AOD than the stations analysed previously which were near dust sources. The
AOD maximum is recorded in April–May, corresponding with maximum MACC-II
dust emissions over Egypt, and western Asia (Fig. 1b), and a secondary maximum
is observed from August to October (Fig. 6j). MACC-II follows rather well the
AOD annual course observed by AERONET, better than MODIS and OMI do, which
overestimate excessively. When considering DOD, the agreement between MACC-II
and AERONET is excellent, and the secondary maximum is smoothed (Fig. 6k). At
the level of daily records, MACC-II captures very well all DOD peaks
(Fig. S4d). This station shows very high correlations (
In the western Mediterranean, we have analysed Granada station. This is
an urban site located in the southern part of the city of Granada
(Spain) situated in the south eastern part of the Iberian Peninsula,
surrounded by mountains of high elevation (Alados-Arboledas et al.,
2008). This station shows low AOD values through the year and is
slightly affected by episodic Saharan dust outbreaks mainly in
summertime (Basart et al., 2009; Lyamani et al., 2010). AERONET and
MACC-II show a major AOD maximum from July to September and
a secondary maximum in February. The agreement between MACC-II and
AERONET is quite good, better than that found between AERONET and
satellite records (Fig. 6
The maximum correlation coefficients are found in summer (June–August), with
monthly values
When the AERONET stations are grouped, the statistics show a lower
correlation in the central Mediterranean (0.60) than in the western and
eastern Mediterranean (0.80 and 0.81, respectively) (Table
The subtropical region is a well-known Saharan dust transport corridor, mainly in summertime (Prospero et al., 1995; Engelstaedter et al., 2006), so it is a good testing bench to evaluate the performance of MACC-II in situations of dust transport. Santa Cruz de Tenerife (SCO), is a station located in Tenerife, Canary Islands, it monitors marine aerosols within the marine boundary layer (MBL) and mineral dust during Saharan outbreaks.
The AOD annual course from MACC-II tracks well that observed by
AERONET and MODIS. A slight overestimation in MACC-II and MODIS is
observed in July and August when the maximum AOD is recorded as
a result of a higher dust intrusions frequency. The agreement between
MACC-II and MODIS is excellent (Fig. 6p), most likely because the data
assimilation from MODIS is successful over the ocean, once the dust
cloud is accurately identified by MODIS. The agreement of MACC-II-AERONET
in DOD is also excellent showing the maximum DOD in summer, and
a secondary maximum in March (Fig. 6q), in agreement with
Alonso-Pérez et al. (2007) and Basart et al. (2009). The daily DOD
records from AERONET and MACC-II show good agreement. MACC-II shows
skill in simulating single dust events in time and in magnitude
(Fig. S3f). We find similar skill scores in the north-western Maghreb
region as it is also a Saharan dust outflow corridor (Table
MODIS shows the best scores (Fig. 7f). MACC-II shows a similar behaviour to that of MODIS in summer, when the major dust intrusions are recorded, and in autumn. OMI clearly departs from the performance of MODIS and MACC-II.
In this study, we also analyse the ability of MACC-II in reproducing climatological dust vertical distribution instead of evaluating its skill to reproduce single extinction vertical profiles.
The extinction vertical profiles simulated by MACC-II (at
550
CALIOP vertical profiles were also analysed and compared with MACC-II
at SCO and M'Bour. CALIOP extinction profiles at 532
The average particle extinction-to-backscatter ratio, hereinafter referred as
the “lidar ratio” (LR) is defined as
In order to ensure that the extinction vertical profiles corresponded
to conditions in which the prevailing aerosol was desert dust, we only
selected those extinction profiles corresponding to
Averaged extinction coefficient vertical profiles obtained
with simultaneous extinction profiles simulated with MACC-II and
observed by the lidar at M'Bour for those days with mean
The months included in each season do not necessarily agree with those used by other authors for this site (e.g. Léon et al., 2009; Cavalieri et al., 2010; Schmechtig et al., 2011; Mortier, 2013). In our case, the four seasons are the dry season (November–March), driven by the Harmattan winds, spring (April–May), the wet period (June–August), basically driven by the monsoon, and autumn (September–October). The averaged extinction vertical profiles of MACC-II and lidar for the different seasons show distinct characteristics in terms of mineral dust vertical distribution (Fig. 8a–d). Regarding the comparison with simultaneous CALIOP extinction vertical profiles, we must point that the number of profiles used in the averages is notably lower (Fig. 8e–h); therefore, the averaged vertical profiles are noisier.
It should be noted that the interannual variability in the
concentration of different types of aerosols over M'Bour is rather
large (León et al., 2009; Mortier, 2013), so the statistics
presented below are not intended to have climatological significance,
but rather to show the average characteristics of the different seasons for
the period 2007–2008. In general, a well-defined MBL is observed from
lidar extinction profiles in all seasons with a high extinction (
A significant result is that MACC-II does not match the observed
extinction within the MBL (Fig. 8a–d). Similar results
are found with CALIOP vertical profiles, although CALIOP intensifies
even more the extinction peak in the MBL in summer and autumn (Fig. 8g
and h). However, these differences between MACC-II and the
ground-based lidar might be explained, at least partially, by an
artefact of lidar extinction retrieval. The lidar ratio applied in
these profiles follows a “single-layer” approach, which uses
a variable lidar ratio that is selected for each profile in order to
achieve the best agreement with the AOD provided by a co-located
AERONET station. Thus the averaged value of lidar
ratio
Above the MBL we observe the impact of desert dust into the free-troposphere. The top of the dust layer, referred to as top layer (TL) according to León et al. (2009), changes according to the season.
During the dry season (250 paired vertical profiles from
November to March), in wintertime, we find an excellent agreement
between MACC-II and lidar (Fig. 8a). This season is characterized by
the presence of biomass burning aerosols confined in the upper layers
(free-troposphere), according to Haywood et al. (2008) and Cavalieri
et al. (2010). However, we expect to have filtered out most of this
contribution. The mean AERONET
In spring (228 paired vertical profiles in April–May) a slight
increase in extinction is observed between 1.5 and 4
In the wet season (163 paired vertical profiles from June to August)
the bulk of the aerosol vertical distribution is found between 2 and
3
Finally, in autumn (45 paired vertical profiles from September to
October), we observe the minimum extinction in low levels of all
seasons, and an extinction maximum centred at
We can conclude that a rather good agreement between lidar, MACC-II
and CALIOP is found at M'Bour station, although a slight
overestimation is observed in CALIOP in upper levels and the MBL. This
agrees with Amiridis et al. (2013) who reported CALIOP extinction overestimation,
compared with BSC-DREAM8b model, above 5 and below 1
The same as Fig. 8 but for SCO station, and corresponding periods, where
For SCO we have established five periods: the winter season (from
December to February), the early spring season (from March to April)
when the Saharan dust outbreaks occur at low altitude (up to
Lidar ratios (LR) at SCO station when
The first notable feature is, like at M'Bour site, the presence of
a layer with relatively high extinction values (0.05–01) within the
MBL, below 1
In wintertime we find a good agreement in extinction vertical distribution
between MACC-II and the lidar in the free troposphere over Tenerife
(73 paired vertical profiles from December to February)
(Fig. 9a). During this season the presence of dust is rare and it is
observed at low levels, being well simulated by MACC-II.
The mean
In early spring (105 paired vertical profiles from March to April) low-level Saharan dust outbreaks intruding the MBL and affecting the
population living in areas close to the coast are recorded almost
every year, although there is a great interannual variability (Viana
et al., 2002; Alonso-Pérez et al., 2007). During this season, dust
intrusions may impact significantly up to
Late spring (29 paired vertical profiles from May to June) is the
cleanest period in which very few dust intrusions occur. However, when
some dust outbreaks sporadically occur, their vertical structure
resemble those recorded in summertime, reaching higher levels (up to
Finally the most interesting season in terms of dust impact is
summertime (129 paired vertical profiles from July to
September). During this season the SAL frequently intrudes the
subtropical free troposphere (Karyampudi et al., 1999) and clearly
impacts the lower free troposphere, from
We highlight that the comparison with CALIOP is not trivial, and that much of the observed differences between CALIOP and MACC-II might be due to a number of varied causes addressed by Burton et al. (2013) and Amiridis et al. (2013) and references herein. Some of them are misclassification of aerosol type and failure to identify different aerosol types within a column (which could have great significance at M'Bour), errors in modelled lidar ratios for particular aerosol types, unsuitable averaging technique of CALIOP extinction vertical profiles, cloud contamination, and the uncertainty of CALIOP extinction profiles. A specific comparison with CALIOP by using a more refined CALIOP data selection methodology, such as that used by Amiridis et al. (2013), would be necessary.
Monthly box-plots of surface dust concentration simulated by
MACC-II and
Surface dust daily mean concentrations from MACC-II reanalysis have
been evaluated with daily averaged
The selection of
The monthly evolution of recorded
MACC-II underestimates monthly means throughout the year and
especially in winter and early spring (dry season) when Saharan dust
is transported by Harmattan winds. This is also observed in the
results of the vertical profiles comparison in Sect. 4.2. During the
dry season, the dust variability recorded along the stations transect
reflects the variability in dust emission by different Saharan sources
(Marticorena et al., 2010). This zonal gradient, with higher
Skill scores quantifying the level of agreement between MACC-II
surface dust concentrations and AMMA-
We computed the skill scores of the comparison of paired daily data
MACC-II-AMMA stations for the period 2007–2008, for each of the three
stations, for the entire period, for the period November–March,
corresponding to the dry season in which the highest
The correlation coefficients show a marked seasonal variation, with
higher values in the dry season and lower values in summertime
affected by regional mesoscale convective systems from monsoon
regime. The correlation coefficient is moderate in M'Bour (
The FGE ranges between 0.68 and 0.79 in Cinzana and Banizoumbou in the
three periods, and from 0.9 to 1.0 in M'Bour. The MNMB is from
To make a proper assessment of the obtained skill scores we have used,
as did Schmechtig et al. (2011), we have compared our skill scores to
the “model performance goal” (the level of accuracy that is
considered to be close to the best a model can be expected to achieve)
and “model performance criteria” (the level of accuracy that is
considered to be acceptable for modelling applications) established by
Boylan and Russel (2006) for PM. According to Boylan and Russel
(2006), the model performance goal has been met when both the FGE and
the mean fractional bias (corresponding to MNMB in Table
The level of agreement between MACC-II surface dust concentration and
observed
A thorough analysis with an emphasis on dust sources over northern
Africa and the Middle East is conducted to evaluate the MACC-II reanalysis
dust through the use of AOD from MISR, MODIS and OMI satellite aerosol
products, ground-based AERONET data, in situ
The geographic domain selected for the validation of MACC-II 2007–2008 reanalysis comprises two of the most arid desert regions of the Earth, the Sahara and the Middle East, world primary mineral dust sources, as well as oceanic regions (Mediterranean Sea, Atlantic Ocean and Arabian Sea) over which dust clouds are often transported. In this broad region, the dust burden can vary by several orders of magnitude. Dust is mixed with marine aerosols in coastal regions, with biomass burning aerosols in the Sahel, and aerosols from industrial activities in the Middle East and the Mediterranean basin. So, it is a complex geographic domain that constitutes a real test bench to know how MACC-II reanalysis behaves in simulating mineral dust. Our aim was to know to what extent this MACC-II reanalysis is able to correctly simulate mineral dust content variations on daily, monthly, seasonal and interannual basis in different regions.
The agreement between MISR, OMI, MODIS and MACC-II is, in general, rather good, reproducing the same AOD spatial patterns. The AOD MACC-II-MISR NMB values fall within 1.4 in most of the study domain in the four seasons, except in the Mediterranean basin, Turkey, Iran and central Africa where the ratios are higher. MACC-II-MISR NMB values are larger than 1.4 over central Sahara in spring and summer, which might be caused by dust storms not observed by MISR due to its low temporal resolution. A notable exception is the MACC-II AOD underestimation over the Bodélé depression which might be related to an underestimation of surface wind velocity over this region. MACC-II generally is able to correctly simulate the interannual variations of AOD in each season obtained by satellite observations, albeit smoother. The dust corridor from Iraq to Oman, covering the entire Persian Gulf, is the region in which MACC-II shows greater difficulties to adequately simulate interannual changes in winter and spring compared with satellite observations.
MACC-II AOD and AE have been also quantitatively evaluated by means of
the comparison against 26 AERONET stations distributed in
different regions. We have used the AERONET (at 500
We have assessed the ability of MACC-II in reproducing dust vertical
profiles. We averaged those extinction vertical profiles simulated by MACC-II
(at 550
Surface dust daily mean concentrations from MACC-II reanalysis has been
evaluated with daily averaged
The evaluation of mineral dust is a complex task because dust is one of the many types of aerosols that can be found mixed in the atmosphere. An important limitation of the validation is the uncertainty associated with dust observation itself. In addition, the assessments at ground stations have the added difficulty of comparing an observed value at a point with a value simulated in a relatively large grid size. A second limitation is the scarcity of observations in desert dust source regions such as northern Africa and the Middle East. Reanalysis data correspond to regions where there are very few ground based observations, and where satellite sensors have major problems to obtain accurate information due to high ground reflectivity. For this reason dust reanalysis data become unique information in this study domain.
The results highlighted in the present study will help not only the climate–weather scientific community but also end-user communities to prevent the impact of severe events over desert source regions where dust is considered to be a harmful air pollutant. Moreover, MACC-II reanalysis could be used in several health-related applications, such as epidemiological studies, or to obtain maps of solar radiation attenuation by mineral dust in suspension, or in ocean research to relate dust deposition with chlorophyll records, among others.
The mean bias (MB), the root-mean-square errors (RMSE), the modified
normalized mean bias (MNMB), also termed mean fractional bias (MFB),
the fractional gross error (FGE), also known as mean fractional error
(MFE) and the Pearson's correlation coefficient (
Metrics used to quantify the level of agreement between MACC-II
simulations and the observations, where
Taylor diagrams (Taylor, 2001) provide a visual framework for comparing model
results or observations to reference observations. The similarity between
model/observations with reference observations is quantified in terms of
their correlation, their centred root-mean-square error (CRMSE), defined in
Table
The standard skill scores and Taylor diagrams presented in this section are used in this work to evaluate the relative skill of MACC-II in comparison with satellite observations using AERONET as the reference (see Sect. 4.1.2).
This work has been supported by EU-project Monitoring Atmospheric
Composition and Climate (MACC-II) under the European Union Seventh
Framework Programme, grant agreement number 283576. Based on
a French initiative, AMMA was developed by an international
scientific group and funded by a large number of agencies,
especially from Africa, European Community, France, UK and USA. More
information on the scientific coordination and funding is available
on the AMMA International web-site: