The MACC reanalysis dust product is evaluated over Europe,
northern Africa and the Middle East using the EARLINET-optimized
CALIOP/CALIPSO pure dust satellite-based product LIVAS (2007–2012). MACC
dust optical depth at 550 nm (DOD
Eolian dust is mainly produced naturally by disintegration of soil
aggregates over deserted, arid and semi-arid areas. The amount of dust
emitted into the atmosphere depends on surface wind speed and on factors such
as soil texture, soil moisture and vegetation cover (IPCC, 2013). Dust is
also produced locally from anthropogenic activities (e.g., manufacturing,
construction, mining, agricultural activities, herding livestock, off-road
vehicles and warfare) (Zender et al., 2004). On a global scale, it has been
estimated that natural sources account for
Climate and the biogeochemical cycles of various ecosystems (terrestrial and oceanic) are affected significantly by dust (Cuevas et al., 2015, and references therein). Dust modulates the radiative budget in the Earth–atmosphere system directly and indirectly by changing the microphysical and macrophysical properties of clouds acting as cloud condensation nuclei (CCN) and ice nuclei (IN) (IPCC, 2013, and references therein). Also, mineral dust affects human health in various ways (e.g., causing allergies, respiratory problems, eye infections, cardiopulmonary diseases, lung cancer), being related to more than 400 000 premature deaths per year on a global basis (Giannadaki et al., 2014; Lelieveld et al., 2015). The deposition of dust can also reduce crop yields and lead to livestock losses (Sivakumar, 2005). Strong episodic dust events hamper visibility, thereby affecting air and road transportation (De Villiers and Van Heerden, 2007) while the deposition of dust on solar panels affects their energy production efficiency (Beattie et al., 2012).
According to Ginoux et al. (2012) 55 % of the global dust emissions originate in northern Africa, with only 8 % being anthropogenic (mostly from the Sahel region). Significant amounts of dust are transported over Europe from the Sahara Desert and the Arabian Peninsula after crossing the Mediterranean Sea (Georgoulias et al., 2016a, and references therein) and also from smaller local sources (see Alastuey et al., 2016). Taking into account the determinant role of dust in processes related to weather and climate, human health, and the economy, it is obvious that adequately simulating the amount of dust and its optical properties over the region is essential.
The World Meteorological Organization (WMO) acknowledged this by establishing
the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) in
2007 that provides daily dust forecasts from more than 15 organizations for
different geographic regions (currently for the northern Africa–Middle
East–European region, for Asia and for the Americas). Among other global and
regional models the MACC (Monitoring Atmospheric Composition and Climate)
aerosol system of the European Centre for Medium-range Weather Forecasts
(ECMWF) (Morcrette et al., 2009; Benedetti et al., 2009) provides 3-day dust
forecasts (optical depth and surface concentration) on a daily basis. The
aerosol forecasts are produced using the same system that was operated for
the production of the multiyear (2003–2012) MACC reanalysis. The MACC
reanalysis was developed within the framework of GMES (Global Monitoring for
Environment and Security) and a series of MACC projects funded by the
European Union and coordinated by ECMWF
(
Upon its release the MACC reanalysis aerosol product has been used in many studies at a global and regional level. For example, it has been used in global estimates of the direct and indirect aerosol radiative effect (Bellouin et al., 2013), to study the sensitivity of clouds to aerosol loads and types over the oceans (Andersen et al., 2016), to constrain the influence of aerosols on cloud coverage (Gryspeerdt et al., 2016), to build regional climatologies in conjunction with satellite data (e.g., Nabat et al., 2013; Georgoulias et al., 2016a), as input for the production and evaluation of satellite-based surface solar radiation products (Mueller et al., 2015; Alexandri et al., 2017), to support reports on the current state of the climate (Benedetti et al., 2014), etc.
Specifically in dust-oriented studies, MACC forecasts have been used in conjunction with measurements from dropsondes and lidars onboard aircrafts, ships and satellites (Cloud-Aerosol Lidar with Orthogonal Polarization onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations – CALIOP/CALIPSO) to study the long-range transport of Saharan dust across the Atlantic within the framework of the Saharan Aerosol Long-range Transport and Aerosol–Cloud-interaction Experiment (SALTRACE) campaign in spring and summer 2013 (Chouza et al., 2016; Ansmann et al., 2017). In these studies forecast fields were used instead of analyses (MACC reanalysis data stop in 2012), focusing on the total aerosol optical depth (AOD) and extinction coefficients rather than on dust. Cuevas et al. (2015) evaluated the MACC reanalysis dust product over northern Africa and the Middle East for 2 years (2007–2008) using ground and satellite-based measurements. Their comparisons focused on specific sites (AERONET sun photometers, lidars and CALIOP/CALIPSO observations) while for spatial evaluations they utilized total AOD satellite data from passive sensors such as MODIS, MISR and OMI. Marinou et al. (2017) furthermore performed a first comparison of the EARLINET-optimized CALIOP/CALIPSO pure dust optical depth (DOD) patterns with MACC reanalysis DOD patterns; however, a detailed 3-dimensional spatiotemporal evaluation of the MACC dust product is still missing.
In this study we advance for the first time to a 3-D (optical depths and
profiles) evaluation of the MACC reanalysis dust product over the Europe–northern Africa–Middle East domain (13–60
In this work, two MACC reanalysis datasets, one characterizing the columnar
dust load and one indicative of the dust profile in the atmosphere, are
evaluated for a 6-year period spanning from 2007 to 2012.
The 3-hourly dust optical depth data at 550 nm (DOD
The MACC reanalysis data used here are produced using the aerosol analysis
and forecast system of ECMWF. This consists of a forward model (Morcrette et
al., 2009) and a data-assimilation module (Benedetti et al., 2009). The MACC
forecasting system assimilates, among other observational data (Eskes et al.,
2015), AOD
For the evaluation of the MACC reanalysis data, dust optical depth at 532 nm
(DOD
Flowchart with the
procedure followed for the evaluation of the columnar
The DOD and profile datasets from MACC reanalysis have to be processed
properly prior to the comparison with the LIVAS data. Generally, it is much
more straightforward to evaluate the MACC columnar dataset. It has to be
mentioned that while the MACC reanalysis data are available on a 3-hourly
basis, the LIVAS data used here are available as monthly means. However, the
exact overpass date and time of the retrievals used for the calculation of
the monthly data is given which allows for the temporal collocation of the
two datasets. The MACC DOD
Much more effort is needed to bring the MACC reanalysis and the
satellite-based profile data together in a format suitable for comparison
(see Cuevas et al., 2015; Chouza et al., 2016, for previous efforts) prior to
the horizontal and temporal collocation of the datasets. As the MACC
reanalysis offers only natural (dust and sea salt) AOD
The MACC reanalysis dust product evaluation procedure comprises different
steps. First the annual MACC DOD
Annual patterns of the MACC DOD
In this section, the evaluation of the MACC columnar dust load is presented.
As shown in Fig. 2a, b the annual MACC DOD
MACC DOD
Over the whole domain (EUNM), the MB between the MACC and LIVAS DOD is 0.025
and the NMB is
It is concluded from the two paragraphs above that in general MACC overestimates DOD for regions with low dust loadings and underestimates DOD for regions with high dust loadings. Similar results were shown in Amiridis et al. (2013) and Tsikerdekis et al. (2017), in which BSCDREAM8b and RegCM4 dust simulations were compared against CALIOP/CALIPSO satellite observations. Many reasons could be responsible for these overestimations and underestimations. First of all, they might be related to the model itself (e.g., parameterization of dust emissions, the wind velocity, the distribution of dust particles in different bins, the dry and wet deposition, the convection scheme which is used). For example if the model overestimates the fine-mode dust particles, the lifetime of dust in the air would increase, leading to the transport of particles away from the sources and at greater height levels. However, as discussed in Ansmann et al. (2017), the uncertainties stemming from the complex parameterizations used by the model make it difficult to reach a solid conclusion about the observed overestimations and underestimations.
Another reason for the underestimation of DOD close to the major dust
sources in the area could be the assimilation of AOD
Seasonal patterns of the MACC DOD
One more parameter contributing to the differences observed between the model
and the observations is probably the limitation of CALIPSO in detecting aerosol
layers with signals lower that the satellite's signal-to-noise ratio (SNR)
(Winker et al., 2013). In particular, in heights where the CALIPSO SNR is
higher than the signal of the layer, the area is characterized as clear air,
and a value of 0 km
Monthly variability of the MACC DOD
In this section, the seasonal variability of the MACC, the LIVAS DODs and
their differences are discussed. The seasonal patterns of MACC DOD
The monthly variabilities of MACC and LIVAS DOD and their MBs over the EUNM domain and over the nine subregions of interest shown in Fig. 4 complement the results discussed above. In general, MACC and LIVAS exhibit similar monthly variability structures despite the significant biases indicating that MACC captures well the observed seasonality of DOD for all subregions. Over the whole EUNM domain DOD is consistently overestimated by MACC throughout the year, November and June being the months with the highest and lowest MB, respectively (Fig. 4j). In line with the previous paragraph, for areas away from the sources, such as CE, EE, SWE, CM, EM and ATL, MACC overestimates DOD during spring, summer and autumn and to a lesser extent in winter (Fig. 4a–f). Over those subregions, DOD is slightly underestimated by MACC only in one case (in February over EM). The overestimation is stronger over the regions of CE and EE. In addition, the overestimation is stronger from late spring to early autumn when MACC shows enhanced DOD values contrary to LIVAS. Over SWE, CM, EM and ATL the monthly variability of MACC DOD is closer to the LIVAS one compared to CE and EE; however, significant biases are observed for spring, summer and autumn. We see here that both MACC and LIVAS depict clearly the difference in the peak period between the western (summer peak), the central (transitional region) and the eastern (spring peak) parts of the Mediterranean Basin. Over CWSah MACC underestimates DOD from February to September when dust loadings peak (Fig. 4g) while over ESah MACC overestimates DOD consistently throughout the year (Fig. 4h). Over ME MACC and LIVAS DODs are very close from February to July while MACC overestimates DOD during the rest of the year (Fig. 4i). In line with the discussion in the previous paragraph DOD peaks in summer over CWSah, in spring over ESah and during spring and summer over ME.
MACC dust extinction coefficient at 550 nm (in km
Patterns of the MACC average dust extinction coefficient at 550 nm
(in km
In this section, the evaluation of the annual MACC reanalysis profiles is
presented, taking advantage of the unique ability of CALIOP/CALIPSO to
retrieve dust extinction coefficient profiles. As discussed in Sect. 2.3, the
extinction coefficient patterns presented in this work are reported at four
1800 m layers that cover the first
The 300 m resolution profiles of the MACC dust extinction coefficient
at 550 nm (in km
In general, over EUNM, the MB between the MACC and LIVAS extinction
coefficients is 0.006 km
Figure 6 shows the 300 m extinction coefficient profiles from MACC and LIVAS
and the corresponding biases for the whole EUNM domain and the nine subregions
of interest. As discussed above we focus on altitudes higher than
1 km a.s.l. to avoid as much as possible the interference of sea salt
aerosols and hence assume that the MACC natural aerosol extinction
coefficients can be similar to dust extinction coefficients. In general, over
EUNM, MACC overestimates extinction coefficients consistently from 1 up to
9 km a.s.l. (Fig. 6j). The overestimation is stable for heights below
The appearance of nonzero extinction coefficients at heights well above
5 km a.s.l. in the MACC aerosol product, in contrast to ground or
satellite-based observations, can be spotted in figures of previous studies
(e.g., Fig. 9 in Cuevas et al., 2015, and Fig. 5 in Ansmann et al., 2017).
However, there has been no effort to understand the reasons for this
situation which has been previously reported for other dust transport models
as well (Mona et al., 2014; Binietoglou et al., 2015). According to the
discussion in Sect. 3.1.1, this might be due to the way the model deals with
the dust distribution in different size bins and dust deposition, vertical
transport and mixing. An overestimation of the fine-mode dust particles, an
underestimation of the dry or wet deposition or a model parameterization that
enhances the vertical transport of dust in the atmosphere would justify the
existence of particles at heights up to 9 km a.s.l. over the whole EUNM
domain (see Tsikerdekis et al., 2017). In addition, the appearance of
nonzero dust extinction coefficients in remote oceanic areas and areas away
from the sources could be related to the MACC assimilation procedure. As
discussed in Sect. 3.1.1 in detail, an underestimation of the modeled total
AOD at each time step over the greater European domain relative to MODIS DT
data is possible as ammonium nitrate aerosols, which can affect the AOD
directly and indirectly through the absorption of water (Karydis et al.,
2016), are not included in the model. In this case, during the assimilation
procedure the concentrations of the various aerosol components and
consequently dust will be enhanced to match the observed AOD. Undetected
aerosol layers by CALIPSO (see Sect. 3.1.1) may also play some role. These
factors or a combination of them could be responsible for the consistent MACC
overestimation at great heights even over regions such as CWSah and ME where
dust extinction coefficients are underestimated by MACC for heights below
Seasonal patterns (DJF: column 1, MAM: column 2, JJA: column 3
and SON: column 4) of the mean bias between the MACC average dust extinction
coefficient at 550 nm (in km
Monthly variability of the MACC–LIVAS dust extinction coefficient
mean bias profiles (in km
In this section, the seasonal variability of the bias between MACC and LIVAS
dust extinction coefficient profiles is discussed. The seasonal patterns of
the biases between the MACC and LIVAS extinction coefficients for the four
reference layers are presented in Fig. 7. In accordance to Fig. 5 the
absolute MB values are higher in layer 1, decreasing in layer 2 and layer 3.
In layer 4 MB is consistently positive during all the seasons. MACC
overestimates extinction coefficients strongly (MB values higher than
0.02 km
The monthly variability of the bias between MACC and LIVAS 300 m dust
extinction coefficient profiles over EUNM and over the nine subregions of
interest is shown in Fig. 8. As was previously suggested (Sects. 2.3 and
3.2.1), we should focus here on altitudes higher than 1 km a.s.l. to avoid
as much as possible the interference of sea salt aerosols in MACC profiles.
Over the whole EUNM domain (Fig. 8j) and for heights lower than
In this work, the MACC reanalysis dust product is evaluated over Europe,
northern Africa and the Middle East (EUNM domain) using CALIOP/CALIPSO
satellite observations for the period 2007–2012. Specifically, MACC dust
optical depth (DOD) data and MACC natural aerosol (dust and sea salt)
extinction coefficient profiles at 550 nm are evaluated against DOD and dust
extinction coefficient profiles at 532 nm from the LIVAS pure
dust product (pure dust is separated from the dust and polluted-dust CALIPSO
subtypes) (Amiridis et al., 2013). As MACC reports only natural extinction
coefficients and not dust extinction coefficients a direct evaluation is
unfortunately impossible. By focusing on heights above 1 km a.s.l. the
influence of sea salt particles (that typically reside at low heights) is
diminished and hence it can be assumed that the MACC natural aerosol profile
data can be similar to the dust profile data, especially over pure
continental regions, while our results should be considered less robust over
the sea and regions close to the coasts. The main findings of this study are
summarized in the following:
The annual MACC DOD The MACC reanalysis DODs exhibit a similar spatial variability with the
LIVAS DODs during all the seasons. The well-documented high dust loadings in
summer and spring, especially over northern Africa and the Middle East, are
captured by both MACC and LIVAS. For areas more remote from the sources, MACC
overestimates DOD during spring, summer and autumn and to a lesser extent in
winter (MB values up to 0.05). MACC strongly underestimates (MB values lower
than In this work, dust extinction coefficient patterns are reported at four
1800 m layers (layer 1: 1200–3000 m a.s.l., layer 2:
3000–4800 m a.s.l., layer 3: 4800–6600 m a.s.l. and layer 4:
6600–8400 m a.s.l.). The MACC and LIVAS dust extinction coefficient
patterns are similar over areas characterized by high dust loadings,
especially for the first three layers. Within these layers the overestimations
and underestimations from MACC are observed in the same areas where MACC
overestimates or underestimates DOD. Layer 4 is characterized by zero or near-zero LIVAS extinction coefficients everywhere, MACC overestimating extinction
coefficients consistently over the whole EUNM domain. The MACC–LIVAS extinction
coefficient MB is 0.006 km The absolute MACC–LIVAS MB values are higher in layer 1, decreasing in
layer 2 and layer 3. In layer 4 MB is consistently positive during all the
seasons. Over the whole EUNM domain and for heights lower than
Overall, it is shown in this work that MACC overestimates DOD for regions
with low dust loadings and underestimates DOD for regions with high
dust loadings. Nonzero MACC DODs
appear over remote areas (away from the source areas in the south) where
LIVAS returns zero DODs. In contrast to LIVAS, nonzero MACC dust extinction
coefficients can be spotted over the whole EUNM for heights up to 9 km a.s.l.
throughout the year. As discussed above, this could be due to the model
performance and its parameterizations of emissions and other processes,
and/or due to the assimilation of AOD
The MACC reanalysis dust data used in this work were
provided by Angela Benedetti (angela.benedetti@ecmwf.int). The MACC
reanalysis data are available to the public either through
The authors declare that they have no conflict of interest.
This research has been financed under the FP7 Programme MarcoPolo (grant
number 606953, Theme SPA.2013.3.2-01). LIVAS has been financed under the
ESA-ESTEC project LIVAS (contract no. 4000104106/11/NL/FF/fk). The
researchers from NOA acknowledge the support of the European Research Council
under the European Community's Horizon 2020 research and innovation framework
program/ERC grant agreement 725698 (D-TECT), the European COST Action InDust
(grant number CA16202) and the Stavros Niarchos Foundation. CALIPSO data were
provided by NASA. We thank the ICARE Data and Services Center
(