ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-13431-2016The impact of the Pacific Decadal Oscillation on springtime dust activity in
SyriaPuBingbing.pu@noaa.govhttps://orcid.org/0000-0002-7620-8460GinouxPaulhttps://orcid.org/0000-0003-3642-2988Atmospheric and Oceanic Sciences Program, Princeton University,
Princeton, New Jersey 08544, USANOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
08540, USABing Pu (bing.pu@noaa.gov)31October2016162113431134486July201611August20167October201614October2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/13431/2016/acp-16-13431-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/13431/2016/acp-16-13431-2016.pdf
The increasing trend of aerosol optical depth in the
Middle East and a recent severe dust storm in Syria have raised questions as
to whether dust storms will increase and promoted investigations on the dust
activities driven by the natural climate variability underlying the ongoing
human perturbations such as the Syrian civil war. This study examined the
influences of the Pacific Decadal Oscillation (PDO) on dust activities in
Syria using an innovative dust optical depth (DOD) dataset derived from
Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol
products. A significantly negative correlation is found between the Syrian
DOD and the PDO in spring from 2003 to 2015. High DOD in spring is associated
with lower geopotential height over the Middle East, Europe, and North
Africa, accompanied by near-surface anomalous westerly winds over the
Mediterranean basin and southerly winds over the eastern Arabian Peninsula.
These large-scale patterns promote the formation of the cyclones over the
Middle East to trigger dust storms and also facilitate the transport of dust
from North Africa, Iraq, and Saudi Arabia to Syria, where the transported
dust dominates the seasonal mean DOD in spring. A negative PDO not only
creates circulation anomalies favorable to high DOD in Syria but also
suppresses precipitation in dust source regions over the eastern and southern
Arabian Peninsula and northeastern Africa.
On the daily scale, in addition to the favorable large-scale condition
associated with a negative PDO, enhanced atmospheric instability in Syria
(associated with increased precipitation in Turkey and northern Syria) is
also critical for the development of strong springtime dust storms in Syria.
Introduction
Dust aerosol is an important component in the climate system that can modify
global and regional energy and water balances (e.g., Tegen et al., 1996;
Miller and Tegen, 1998; Miller et al., 2004; Lau et al., 2009; Yue et al.,
2010, 2011; Choobari et al., 2014; Huang et al., 2014). Dust particles
interact with both solar and terrestrial radiation, modifying temperature
profile and hydrological cycle, which impact regional and global climate. For
instance, studies found that mineral dust influences the strength of the West
African monsoon (e.g., Miller and Tegen, 1998; Miller et al., 2004; Yoshioka
et al., 2007; Solmon et al., 2008, 2012; Mahowald et al.,
2010; Strong et al., 2015)
and Indian summer monsoonal rainfall (Vinoj et al., 2014; Jin et al., 2014,
2015, 2016; Solmon et al., 2015; Kim et al., 2016). Dust particles can also
serve as ice cloud nuclei and influence the microphysical and macrophysical
properties of the cloud (e.g., Levin et al., 1996; Rosenfeld et al., 1997;
Wurzler et al., 2000; Nakajima et al., 2001; Bangert et al., 2012), including
its droplet size, number concentration, lifetime, and albedo, and in turn
affecting the regional radiative budget and hydrological cycle. Mineral dust
also provides nutrients for ocean phytoplankton, affecting ocean productivity
and therefore carbon and nitrogen cycles and ocean albedo (e.g., Fung et al.,
2000; Shao et al., 2011). Strong dust storms also have severe social and
health impacts (e.g., Morman and Plumlee, 2013), affecting public
transportation and causing damage to the eye and lung.
The Middle East is one of the dustiest regions in the world, and recent
studies suggest an increasing trend of aerosol optical depth (AOD) largely
due to dust emission (e.g., Pozzer et al., 2015; Klingmüller et al.,
2016). A once-in-ten-years severe dust storm recently occurred in Syria
during 6–9 September 2015, and raised much attention. More than one
thousand people in Syria were hospitalized due to breathing
difficulties
.
The causes of such a strong dust storm are not fully understood, but there
have been speculations that the ongoing civil war in Syria is largely
responsible for it. The argument is that crop fields were abandoned or
destroyed by the war, so soil dust is easier to be uplifted by wind from
these unprotected land fields. More importantly, will severe dust storms like
the one above increase in the future?
Notaro et al. (2015) studied the dust activities over the Arabian Peninsula
and related the increased dust activities during 2007–2013 to the persistent
dry condition over the “Fertile Crescent” (namely Syria, Iraq, Israel, and
Jordan) primarily caused by a combined effect of La Niña and a negative
phase of the Pacific Decadal Oscillation (PDO). Associated with the drought
is crop failure and increased dust activities over the Arabian Peninsula.
While others, such as Chin et al. (2014) found that the positive trend of
dust emission over the Middle East from 2000 to 2009 is related to increased
surface wind speed, and Klingmüller et al. (2016) attributed the positive
trend of aerosol optical depth over the region (mainly Saudi Arabia, Iraq,
and Iran) from 2001 to 2012 to the combined effects of decreased
precipitation and soil moisture over Iraq and adjacent areas and enhanced
surface wind over the Africa Red Sea coastal area that increased dust
emission.
The above studies suggested that remote sea surface temperature (SST), local
precipitation, surface wind, and vegetation all influence the dust activities
over the Middle East. Among these factors, the remote forcings (such as
tropical Pacific SST) and the PDO not only affect the precipitation
variations over the region but can also influence local circulation including
near-surface winds, both of which influence dust emission. It is thus quite
important to understand the influences of these low-frequency long-lasting
forcings on dust activities underlying the ongoing human perturbations such
as civil war, land use change, and anthropogenic emission.
In this paper we examine the influence of the PDO on the variations of dust
activities in Syria from 2003 to 2015 using Moderate Resolution Imaging
Spectroradiometer (MODIS) Deep Blue dust optical depth (DOD). Previous
studies on the connection between the PDO and dust activities in the Arabian
Peninsula mainly focus on precipitation (e.g., Notaro et al., 2015; Yu et
al., 2015). Here we explore thoroughly how the PDO influences the key factors
associated with the dust activities in Syria on the interannual and daily
scales.
The following section presents the data and methodology used in the paper.
The co-variations of DOD in Syria and PDO from 2003 to 2015 are presented in
Sect. 3 and their physical connections are analyzed in detail in Sect. 4. We
also discussed the long-time connection (e.g., 1948–2015) between Syrian DOD
and the PDO inferred from the reanalyses and observations in Sect. 4. In
Sect. 5, we examined the key factors associated with strong spring dust
events in Syria using daily reanalysis and observations. To what extent
climate models can capture the connections between the PDO and Syrian dust
activities is discussed using the Geophysical Fluid Dynamics Laboratory
(GFDL) AM3 model in Sect. 6. Major conclusions are summarized in Sect. 7.
Data and methodologySatellite and observational datasets Dust optical depth
Daily and monthly dust optical depth data are derived from MODIS aerosol
products retrieved using the Deep Blue (M-DB2) algorithm, which employs
radiance from the blue channels to detect aerosols over bright land surfaces
(e.g., desert). Because surface reflectance is low at blue channels,
increases of reflectance and spectral contrast indicate the presence of
aerosols (Hsu et al., 2004, 2006). Ginoux et al. (2012) used collection 5.1
level 2 aerosol products from MODIS aboard the Aqua satellite to derive DOD.
Here, both MODIS aerosol products (collection 6, level 2) from the Aqua and
Terra platforms are used for cross-validation. Terra passes the Equator from
north to south around 10:30 local time while Aqua passes the Equator from
south to north around 13:30 local time. While the frequency of the maximum
daily 10 m wind in Syria peaks in the afternoon, later than the passing time
of both Terra and Aqua, the averaged maximum total wind speed is nearly
evenly distributed over a day (Fig. S1 in the Supplement). Thus, the results
from both platforms are likely to provide a more complete picture of dust
activities than either one alone.
Aerosol products such as AOD, single scattering albedo, and the
Ångström exponent are first interpolated to a regular 0.1∘ by
0.1∘ grid using the algorithm described by Ginoux et
al. (2010). The dust
optical depth is then derived from AOD following the methods of Ginoux et
al. (2012) with adaptions for the newly released MODIS Collection 6 aerosol
products. To separate dust from other aerosols, we use the Ångström
exponent, which has been shown to be highly sensitive to particle size (Eck
et al., 1999) and single scattering albedo which is less than one for dust
due to its absorption of solar radiation. Instead of using negative values of
the Ångström exponent as done by Ginoux et al. (2012), we use a
continuous function relating the Ångström exponent to fine-mode
aerosol optical depth established by Anderson et al. (2005; their Eq. 5)
based on ground-based data. The DOD data are available from January 2003 to
December 2015.
Daily and monthly DOD indices are formed by averaging DOD data in Syria
between 34–36.5∘ N and 36.5–41∘ E to characterize dust
activities. The averaging area covers most of Syria. We also tested using a
smaller averaging box (33.5–36∘ N, 36.5–39∘ E) for the
DOD index, and the results are similar.
Precipitation
Version 7 of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite
Precipitation Analysis (TMPA) daily product (3B42) is used. This product
covers from 50∘ S to 50∘ N with a spatial resolution of
0.25∘ by 0.25∘ and is available from 1998 to present. Several
important changes are applied to the version 7 product, including using
additional satellite data such as early records of Microwave Humidity Sounder
(MHS) and operational Special Sensor Microwave Imager (SSM/I) records, using
a new infrared brightness temperature dataset before the start of the Climate
Prediction Center (CPC) 4 km Merged Global IR dataset, using a single
uniformly processed surface precipitation gauge analysis, using a
latitude-band calibration scheme for all satellites, and adding output fields
in the data files (Huffman and Bolvin, 2014).
Precipitation Reconstruction over Land (Chen et al., 2002; hereafter PRECL)
from the National Oceanic and Atmospheric Administration (NOAA) is a global
analysis available monthly from 1948 to present at a 1∘ by 1∘
resolution. The dataset is derived from gauge observations from the Global
Historical Climatology Network (GHCN), version 2, and the Climate Anomaly
Monitoring System (CAMS) datasets.
The monthly precipitation of the Climatic Research Unit (CRU) time series
(TS) 3.23 (Harris et al., 2014) is also used as a supplement to the PRECL.
CRU TS 3.23 dataset covers 1901–2014, with a spatial resolution of
0.5∘ by 0.5∘ over land (excluding Antarctica). The gridded
data are based on the analysis of over 4000 individual weather station
records.
Temperature
The Hadley Centre sea ice and sea surface temperature (HadISST) dataset
(Rayner et al., 2003) from the UK Met Office is available monthly from 1870
to the present with a horizontal resolution of 1∘ by 1∘ grid.
Monthly SST from HadISST and land surface temperature from CRU TS 3.23
(0.5∘ by 0.5∘) from 1948 to 2015 are used to examine
temperature patterns associated with dust activities.
Leaf area index (LAI)
LAI characterizes the canopies of plants. It is defined as the one-sided
green leaf area per unit ground area in broadleaf canopies and as half the
total needle surface area per unit ground area in coniferous canopies. LAI at
zero is considered as bare ground while around 10, dense forests. Monthly LAI
is derived from the version 4 of Climate Data Record (CDR) of Advanced Very
High Resolution Radiometer (AVHRR) surface reflectance (Claverie et al.,
2014) and produced by NASA's Goddard Space Flight Center (GSFC) and the
University of Maryland. The gridded monthly data are on a 0.05∘ by
0.05∘ horizontal resolution and are available from 1981 to present. A
detailed discussion on the algorithm and evaluation of the dataset can be
found by Claverie et al. (2016).
Monthly MODIS LAI level 4 data on the Aqua platform (MYD15A2) are also used
for 2003–2015. The original data files were obtained via personal
communication (Ranga Myneni and Taejin Park; Boston
University, 2016) and then reprocessed to fill the missing data by Paul Ginoux.
The horizontal resolution of the data is 0.1∘ by 0.1∘.
Reanalyses
Daily and monthly geopotential height, horizontal winds, and specific
humidity from the National Centers for Environmental Prediction
(NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et
al., 1996, hereafter NCEP1) from 1948 to 2015 are used. Its horizontal
resolution is 2.5∘ by 2.5∘ and has 17 vertical levels from
1000 to 10 hPa, with 8 levels between 1000 and 300 hPa. This reanalysis is
used primarily in this study due to its long record.
ERA-Interim (Dee et al., 2011) from the European Centre for Medium-Range
Weather Forecasts (ECMWF) is a global reanalysis with a horizontal resolution
of T255 (about 0.7∘ or 80 km) and 37 vertical levels, available from
1979 to present. Monthly and four (two) times daily analysis (forecast)
variables are used. The time coverage of ERA-Interim is shorter than the
NCEP1 but its high resolution supplements the latter. Monthly horizontal
winds and geopotential heights are compared with the NCEP1 in the same period
(1979–2015) and show similar features (see discussion in Sect. 3). Daily
10 m and 850 hPa winds and forecast variables such as precipitation are
used to investigate the key factors associated with dust activities.
Climate indices
The PDO and Niño 3.4 indices are downloaded from the website of NOAA
Climate Prediction Center
(http://www.esrl.noaa.gov/psd/data/climateindices/list/). The monthly
standardized PDO index is derived from the leading principal component of SST
anomalies in the northern Pacific Ocean (20∘ N). The monthly
mean of global mean SST anomalies is removed in the PDO index; thus the
influence of global warming is not included. The data are available from 1948
to present. The monthly Niño 3.4 index is derived from the extended
reconstructed sea surface temperature (ERSST v4; Huang et al., 2015, 2016;
Liu et al., 2015) averaged over the tropical Pacific between
5∘ N–5∘ S and 120–170∘ W and is available from
1950 to present.
Model output
To examine the relationship between the Syrian dust activities and the PDO,
the output from the atmospheric component (AM3) of a general circulation
model (CM3; Donner et al., 2011) developed at the GFDL is used. The
finite-volume algorithms described in Lin and Rood (1996, 1997) and
Lin (1997, 2004) are used in the dynamic core in AM3. Different from earlier
versions of the model, a general curvilinear coordinate system is used and
has largely improved the computational efficiency. A hybrid vertical
coordinate (Simmons and Burridge, 1981) of 48 layers is used, with the top
model layer at about 1 Pa (∼ 86 km). AM3 calculates the mass
distribution and optical properties of aerosols according to their emission,
chemical production, transport, and dry and wet deposition. The dust source
function follows the scheme of Ginoux et al. (2001), which places
preferential sources in topographic depressions. The simulated aerosol
optical depth and co-albedo show improved correlations with the AErosol
RObotic NETwork (AERONET) station observations than earlier version of the
model (CM2.1) but slightly underestimate AOD in the Middle East.
A historical run is conducted using the observed monthly SSTs from the Hadley
Centre to drive the AM3. The simulated wind is nudged toward the NCEP/NCAR
reanalysis with a relaxation timescale of 6 h (Moorthi and Suarez, 1992),
similar to the method used by Li et al. (2008). A moderate horizontal
resolution of 2∘ by 2.5∘ is used. The simulation was
conducted from 1948 to 2010. Results from 1960 to 2010 are presented. The
simulation provides a slightly longer time series, compared to the satellite
data, to examine the relationship between Syrian DOD activities and the PDO.
The co-variations between springtime DOD in Syria and the PDO
during 2003–2015
Notaro et al. (2015) related low-frequency variations of monthly
precipitation over Syria and Iraq to the El Niño–Southern Oscillation
(ENSO) and the PDO and thus built the connection between dust activities and
Pacific SSTs. Here we focus on Syria. Figure 1 shows the time series of
monthly Syrian DOD indices (seasonal cycle removed) from both MODIS Aqua
(green) and Terra (grey) platforms and the negative PDO index from
January 2003 to December 2015. The variations of the DOD and the negative PDO
indices are quite similar, showing strong decadal variations underlying
interannual variations. Both are relatively weak during 2003–2007,
relatively strong during 2007–2012 and have become relatively weak since
2013. Note that the DOD indices increase again in 2015 in association with
the severe dust storm in September. The correlation between monthly DOD
indices and the PDO index is -0.51 (Aqua) and -0.50 (Terra) from 2003 to
2015. Other indices, such as Niño 3.4 and Syrian LAI also have
significant but lower correlations with the DOD indices (Table 1).
Monthly time series of Aqua (green) and Terra (grey) DOD indices
averaged over Syria (see Fig. 2 for domain) and PDO index (orange;
multiplying by -1 to show its negative correlation with the DOD indices).
Correlations between monthly DOD indices and the PDO, Niño 3.4
indices, precipitation from the CRU TS3.23 (P1; 2003–2014) and PRECL (P2),
AVHRR LAI (LAI1), MODIS Aqua LAI (LAI2) and 10 m wind speed from the
ERA-Interim averaged over Syria (see the box in Fig. 2) for all the months
(seasonal cycles are removed) from 2003 to 2015 (or 2014). Coefficients
significant at the 95 % confidence level are in bold.
PDONiño 3.4P1P2LAI1LAI210 m windAqua DOD–0.51–0.24-0.05-0.05–0.35–0.41-0.00Terra DOD–0.50–0.23-0.05-0.06–0.32–0.39-0.02
ENSO is known to influence precipitation over the Middle East, by decreasing
precipitation in La Niña years and increasing precipitation in El
Niño years (e.g., Price et al., 1998; Mariotti et al., 2005; Mariotti,
2007, Chakraborty et al., 2006; Barlow et al., 2002; Wang et al., 2014; Yu et al., 2015; Banerjee and
Kumar, 2016), and its influence is generally stronger in La Niña years
(e.g., Wang et al., 2014). Previous studies also showed a comparable
influence of the PDO on precipitation over the “Fertile Crescent” region
including Syria and Iraq vs. that by ENSO (correlations of 0.52 vs. -0.57
from 1979 to 2013, Notaro et al., 2015). The correlations in this study
indicate that the PDO plays a greater role than ENSO in modulating dust
activities in Syria in the recent decade. The PDO is not completely
independent of ENSO, but can be viewed as a phenomenon driven by multiple
physical processes, including the tropical Pacific SST, atmospheric noise,
Aleutian low, Kuroshio–Oyashio Extension, Pacific–North American pattern,
Rossby wave breaking, etc. (e.g., Evans et al., 2001; Newman et al., 2003;
Schneider and Cornuelle, 2005; Strong and Magnusdottir, 2009; Mills and
Walsh, 2013). While some modeling studies suggested that up to half of the
variance of the PDO can be explained by ENSO (e.g., Alexander et al., 2002;
Liu and Alexander, 2007), others found that certain parts of extra-tropical
Pacific SST variability are totally independent of ENSO (e.g., Zhang et al.,
1997; Deser and Blackmon, 1995; Zhang and Delworth, 2015). Here the
correlation between the monthly PDO and Niño 3.4 indices is 0.63 from
January 2003 to December 2015, suggesting that statistically the Niño 3.4
index explains about 40 % of the variances of the PDO index.
Correlation between the PDO index and MODIS (a) Aqua and
(b) Terra DOD in MAM from 2003 to 2015. (c) Correlation
between Syrian DOD indices (navy and green bars denote Terra and Aqua,
respectively) and PDO index for each month and annual mean (ANN) from 2003 to
2015. Red dashed lines denote the 95 % confidence level (t test). Red box
denotes the averaging area for Syrian DOD index.
Figure 2 shows the correlation between the PDO index and Aqua and Terra DODs
in spring, along with correlations between the Syrian DOD indices and PDO
index in individual months and as an annual mean from 2003 to 2015. The
correlation pattern between the PDO index and Aqua DOD is very similar to
that associated with Terra DOD (Fig. 2a–b), with negative correlations over
most areas of Syria and a stronger correlation over eastern Syria than the
western part. DOD over Iraq and northern Saudi Arabia is also significantly
negatively correlated with the PDO. The correlation between the monthly PDO
and Syrian DOD indices shows a persistent negative relationship through the
year, with higher correlations during March–April–May and also in
July–August and December (Fig. 2c). The low correlation in September is due
to the severe dust storm in 2015. The seasonal mean correlations between the
DOD indices and the PDO index in MAM are -0.90 for both the Aqua DOD and
Terra DOD indices, again much higher than their correlations with other
indices (Table 2).
Same as Table 1 but for MAM average from 2003 to 2015 (or 2014).
Coefficients significant at the 95 % confidence level are in bold.
PDONiño 3.4P1P2LAI1LAI210 m windAqua DOD–0.90–0.60-0.14-0.38–0.60–0.640.31Terra DOD–0.90–0.60-0.10-0.34–0.57–0.610.30
The connection between the Syrian DOD and PDO is further examined in Fig. 3,
which shows the correlations between the Syrian DOD indices and SST and land
surface temperatures during MAM and on the annual mean, along with SST and
land surface temperature patterns associated with the PDO index. As shown in
Fig. 3a–b, the SST pattern associated with the positive phase of the PDO in
spring is quite similar to that in the annual mean, with anomalous warm SST
over the tropical Pacific and along the east basin of the North Pacific and
anomalous cold SST over the subtropical central to western North Pacific. SST
in the Indian Ocean is also positively associated with the PDO. But the
correlation between land surface temperature and the PDO is not significant
in most regions, including the Arabian Peninsula.
Correlation between the PDO index and HadISST (over the ocean) and
CRU TS3.23 near-surface temperature (over land) for (a) MAM and
(b) annual mean during 2003–2014. Correlation between
(c)–(d) Aqua and (e)–(f) Terra DOD
indices, and HadISST and CRU near-surface temperature for (c),
(e) MAM and (d), (f) annual mean during
2003–2014. Areas significant at the 95 % confidence level (t test) are
dotted.
Correlations between Aqua DOD index and SST in MAM are nearly opposite to
those of the PDO, with negative correlations over the tropical and eastern
Pacific but positive correlations over the central North Pacific (Fig. 3c).
Correlations between the annual mean Aqua DOD index and SST and land surface
temperature are similar to that in spring, but with slightly weaker magnitude
over the central Pacific (Fig. 3d). The temperature patterns correlated with
the Terra DOD index are quite similar to that of Aqua DOD index (Fig. 3e–f).
Neither DOD indices are significantly correlated with the land surface
temperature over the Middle East in spring.
Since Figs. 2 and 3 indicate a stronger negative correlation between the PDO
and Syrian DOD index during MAM than on annual mean or other seasons, the
following analysis focuses on their connections in spring. Spring is also the
time when Syrian DOD is high and dust storms are most active (Figs. S2 and 3
in the Supplement).
How does the PDO affect Syrian dust activities in spring?
In this section we explore the mechanisms underlying the strong negative
correlations between the PDO and Syrian DOD indices. Since MODIS DOD only
covers the recent decade and the PDO has low-frequency decadal variations
with cycles of about 15–25 and 50–70 years (e.g., Minobe, 1997; Mantua and
Hare, 2002), it is difficult to assess their long-term (i.e., beyond the
recent decade) relationship directly. We tried a method to indirectly verify
their connections during 1948–2015 using high-quality reanalyses and
observation. The PDO shows a dominant negative phase during the late 1940s to
1970s, turns into a dominant positive phase during the 1980s and 1990s, and
has become mostly negative again since the mid-2000s (e.g., JISAO/University
of Washington website
). Therefore the
NCEP1 reanalysis covers about one 50–70-year multi-decadal cycle of the PDO,
while the EAR-Interim covers about half of the cycle.
We first compare the patterns of circulation and precipitation associated
with DOD and PDO indices during 2003–2015 to identify key meteorological
conditions associated with Syrian DOD activities and how these conditions
are connected with the PDO. Then we compare the influences of the PDO on
meteorological conditions during 2003–2015 to a longer period (e.g.,
1948–2015). If the PDO shows relatively consistent influences on the
meteorological conditions that are critical to Syrian DOD activities, then
the influences of the PDO on Syrian DOD are also likely to persist during
1948–2015, assuming that the connection between Syrian DOD activities and
those meteorological conditions does not change much during 1948–2015.
Regression of 850 hPa (shading) and 200 hPa (blue contours; solid
lines for positive values and dashed lines for negative values, from -40
to 40 gpm with an interval of 10 gpm, zero line is not shown) geopotential
heights onto the standardized PDO index for (a) 2003–2015 and (c) 1948–2015
and onto the standardized (b) Aqua and (d) Terra DOD indices. Areas
significant at the 95 % confidence level (t test) are dotted.
How does the PDO affect the circulation over the Middle East? Figure 4 shows
the regression of 200 hPa (contours) and 850 hPa (shading) geopotential
heights onto the standardized PDO and DOD indices. Figure 4a shows that in
the positive phase of PDO, stationary waves propagate from the North Pacific
through North America, the northern Atlantic, and Europe to the Middle
East. The regressions at
850 hPa are generally similar to those at 200 hPa, indicating an equivalent
barotropic structure. Anomalous positive geopotential height over the Arabian
Peninsula is associated with a positive PDO during 2003–2015 (Fig. 4a) and
also during 1948–2015 (Fig. 4c). The geopotential height patterns associated
with both the Aqua and Terra DOD indices are nearly opposite to those
associated with the positive PDO index, with anomalous highs over the central
North Pacific and northeastern North America and anomalous
lows over the west coast of North America, Europe, and the Middle East
(Fig. 4b, d).
Figure 4 suggests that the PDO can influence the variations of springtime DOD
in Syria through its modification on the circulation over the Middle East. A
negative PDO reduces the geopotential height both at 850 and 200 hPa over
the Arabian Peninsula, a scenario that favors high DOD in spring.
Regressions of NCEP1 850 hPa geopotential height (shading; gpm)
and horizontal winds (vectors; m s-1) onto the standardized PDO index
during (a) 2003–2015 and (c) 1948–2015, and onto the standardized (b) Aqua
and (d) Terra DOD indices during 2003–2015. Area where the regression is
significant at the 95 % confidence level (t test) is dotted, and vectors
significant at the 90 % confidence level are plotted in blue.
The connection between Syrian DOD and 850 hPa winds and geopotential heights
are further examined in Fig. 5. Figure 5a shows that a positive PDO is
associated with anomalous easterly winds north of 40∘ N, anomalous
northerly winds over the central Mediterranean Sea around 15∘ E, and
weak southeasterly winds over the eastern Mediterranean Sea and western Syria, in the recent decade (Fig. 5a) as well as from 1948 to 2015 (Fig. 5c). Anticyclonic
winds are located over Oman and Yemen at the south coast of Arabian Peninsula
accompanied by an anomalous high. Positive geopotential height anomalies are
also located over the northwestern Africa and East Africa. On the other
hand, winds associated with high Syrian DOD indices are anomalous westerlies
over the northern Mediterranean basin and Syria (Fig. 5b and d). Anomalous
cyclonic flows are located over the southern Arabian Peninsula, nearly
opposite to those associated with the positive phase of the PDO. The overall
lower geopotential height over the Middle East and Africa also facilitates the
formation of the cyclones (such as Sharav cyclones) that are important for
the spring peak of dust storms in the northern Arabian Peninsula (e.g., Israelevich
et al., 2003; Dayan et al., 2008, 2012).
The regression patterns of 850 hPa winds and geopotential height onto the
PDO and DOD indices in the ERA-Interim are generally consistent with those
shown in the NCEP1 (Fig. S4).
Regressions of ERA-Interim 10 m horizontal winds (green vectors;
m s-1) and cubic wind speed (shading; m3 s-3) onto a
standardized PDO index (a) from 2003 to 2015 and (c) from
1979 to 2015 and onto the standardized (b) Aqua and
(d) Terra DOD indices. Areas where the regressions of the wind speed
are significant at the 95 % confidence level are dotted and vectors
significant at the 90 % confidence level are plotted in blue (t test).
Next we examine the associated variations of near-surface wind and
precipitation that are tied to these geopotential height and low-level wind
patterns. Figure 6 shows the regression of 10 m winds (vectors) and the
cubic 10 m wind speed (shading) in the ERA-Interim onto standardized PDO
index and onto the Aqua and Terra DOD indices. The ERA-Interim is chosen here
because of its higher horizontal resolution compared to the NCEP1, and thus
is more suitable to examine surface wind variations associated with dust
blasting in small scales. Cubic wind speed is used here as the classical dust
emission scheme relates dust flux to the third power of 10 m horizontal
winds. The patterns of the surface wind associated with the DOD indices
(Fig. 6b and d) are largely similar to that of winds at 850 hPa (Figs. 5 and
S4). Anomalous southwesterly winds from coastal North Africa and over the
Mediterranean Sea tend to bring dust from North Africa to Syria. Such a route
of dust transport has not been directly examined, but was discussed in back-trajectory studies on the airflow patterns onto Israel (e.g., Dayan, 1986).
Earlier studies also have suggested a transport of dust from North Africa to
the Mediterranean basin (e.g., D'Almeida, 1989; Moulin et al., 1998; Kubilay
et al., 2000). Anomalous northerly flow over the Red Sea and the west coast
of the Arabian Peninsula (and a weaker wind speed) and anomalous southerly
flow (and a stronger wind speed) over the eastern peninsula also suggest a
transport of dust from the source regions in the middle and southern Arabian
Peninsula, e.g., An Nafud and Ad Dahna deserts, dry riverbeds such as Al
Batin, Al-Rimah, and Al Sahba and Rub' al Khali sandy desert in Saudi Arabia
(Ginoux et al., 2012). An Nafud and Ad Dahna deserts are major sources of
dust storms in Saudi Arabia in spring to early summer (Notaro et al., 2013)
and can also be an important source for Syrian DOD. A modeling study on the
sources of spring DOD in Syria also confirms that North Africa (including
Libya, Algeria, and Egypt) is the largest source with the secondary source in
the Arabian Peninsula (Iraq and Saudi Arabia), and the overall transported
DOD is much higher than local DOD in Syria in spring (Ginoux,
2015).
Variations of surface wind associated with the PDO are different from that
associated with high DOD, with nearly opposite patterns of cubic wind speeds
over the Arabian Peninsula (Fig. 6a and c). When the PDO is positive,
anomalous northerly winds pass through the Mediterranean Sea and turn into
westerly over the northeastern Africa, which may bring dust from Africa to
Israel, Jordan, and Syria as well as increase the moisture transport from the
Mediterranean Sea (Fig. 6a). The anomalous southerly wind from the Red Sea
also brings moisture onto Syria and tends to promote precipitation. Over
southwestern Syria, the anomalous southerly wind is from the less dusty area
over northwestern Saudi Arabia and is less likely to enhance Syrian DOD. A
weak anomalously westerly flow over northeastern Saudi Arabia around
30∘ N and 45∘ E tends to block the northward transport of
dust from the southern and middle Arabian Peninsula to Syria. This westerly
flow weakens if extending the time period of regression to 1979 (Fig. 6c). A
stronger anomalously westerly flow along the west coast of Saudi Arabia and a
southerly flow from the south coast of the Arabian Peninsula are also found
in association with the positive PDO during 1979–2015, bringing moisture
onto Syria. The discrepancies between the regression patterns in the recent
decade and those during 1979–2015, e.g., over the eastern Mediterranean Sea
(Fig. 6a and c), are likely associated with the decadal variations of the
PDO, indicating an instable connection between the PDO and surface winds in
some areas.
Correlation between PRECL precipitation and PDO index during (a) 2003–2015, (c) 1948–2015 and between precipitation and (b) Aqua and (d) Terra
DOD indices during 2003–2015. Areas where the correlation coefficients
are significant at the 95 % confidence level (t test) are dotted.
The anomalous precipitation patterns correlated with the PDO and DOD indices
are shown in Fig. 7. The PDO has a mild connection with precipitation over
the Middle East in spring on the interannual timescale. Similar correlation
patterns are found in previous studies that correlated the PDO with
low-frequency (i.e., > 7 years) variations of precipitation (e.g., Dai,
2013, Fig. 2c). A positive phase of the PDO is associated with increased
precipitation over most areas of the Arabian Peninsula, Turkey, western Iran,
and northeastern Africa over Libya and Egypt in the recent decade and during
1948–2015 (Fig. 7a and c). Patterns are similar during 1979–2015 (not
shown). On the other hand, high DOD in Syria is associated with reduced
precipitation over the Arabian Peninsula (particularly Iraq and central and
southern Saudi Arabia), Egypt, eastern Algeria, and western Libya. This is
consistent with Fig. 6, which indicates dust transport from these areas to
Syria in the spring.
Regression of vertically integrated mass weighted monthly moisture
flux (vectors; kg m-1 s-1) and its magnitude (shading) onto
standardized PDO index during (a) 2003–2015 and (c) 1948–2015, and onto the
standardized (b) Aqua and (d) Terra DOD indices. Moisture flux is integrated
from surface to 300 hPa. Areas with magnitude of moisture flux significant
at the 90 % confidence level are dotted, and moisture fluxes significant
at the 90 % confidence level are plotted in purple vectors (t test).
Figure 8 shows the vertically integrated mass-weighted moisture flux (vector)
and its magnitude (shading) onto the standardized PDO and DOD indices in MAM
from 2003 to 2015 and also onto the standardized PDO index during 1948–2015.
Consistent with the precipitation regression patterns (Fig. 7), anomalous
anticyclonic moisture flux brings more moisture onto the Arabian Peninsula
from the Red Sea and Gulf of Aden associated with a positive PDO (Fig. 8a and
c). Moisture flux over northeastern Africa is also enhanced. Conversely, high
DOD in Syria is associated with an anomalous cyclonic flux centered over the
south coast of the Arabian Peninsula, which reduces moisture flux onto the
southern Arabian Peninsula (Fig. 8b and d). Moisture flux over northeastern
Africa is also reduced, associated with an anomalous cyclonic flow over
the Republic of the Sudan and Egypt. The pattern of the anomalous moisture fluxes associated with PDO
and DOD indices are quite similar to that of the 850 hPa winds (e.g.,
Fig. 5), indicating a dominant role played by low-level moisture transport.
Regression of vertically integrated MSE (104 J m-2) over
the lowest four atmospheric layers (1000, 925, 850, and 700 hPa) from the
NCEP1 onto the standardized PDO index for (a) 2003–2015 and (c) 1948–2015
and onto the standardized DOD indices from MODIS (b) Aqua and (d) Terra
during 2003–2015. Areas significant at the 90 % confidence level (t test)
are dotted.
These anomalous circulation and moisture flux patterns are also linked to the
stability of the lower atmosphere. Figure 9 shows the regression of low-level
moist static energy (MSE; integrated from surface to 700 hPa) onto the
standardized PDO and DOD indices. MSE is defined as a sum of sensible,
latent, and geopotential energy in a column air, i.e., MSE =cpT+Lq+gz, where cp is the specific heat of air at constant pressure, T is
air temperature, L is the latent heat of vaporization of water, q is
specific humidity, g is the gravity acceleration, and z geopotential
height. MSE increasing with altitude denotes a stable atmosphere, so high MSE
in the lower atmosphere indicates an instable condition, and vice versa. The
patterns of the anomalous MSE are tied to the changes of moisture flux and
precipitation anomalies. Reduced MSE is located over large areas of the
Arabian Peninsula, particularly the southwest coast, in association with high
DOD in Syria, indicating a more stable low-level atmosphere and thus less
precipitation (Fig. 9b and d). Such a low MSE is also found over North
Africa, but in a weaker magnitude. The pattern associated with a positive PDO
is nearly opposite, with increased low-level MSE over the Arabian Peninsula,
Red Sea, and along the east coast of Egypt, denoting an instable atmosphere
associated with anomalous moisture transport (Fig. 8a and c) and promoting
convection and precipitation (Fig. 9a and c).
In short, Figs. 4–9 show spring circulation and precipitation patterns
favorable to high DOD in Syria. Anomalous low pressure over Europe, the
southern Arabian Peninsula, and northeastern to eastern Africa promotes
westerly winds from North Africa and southerly flow over the southeastern
Arabian Peninsula, both of which tend to transport dust to Syria. The
anomalous moisture fluxes associated with the geopotential height and wind
anomalies also favor a dry and stable condition over the dust source regions
adjacent to Syria, such as Saudi Arabia, Iraq, and North Africa. The
circulation and precipitation patterns associated with a positive PDO are
largely opposite to those associated with high DOD in Syria in the recent
decade, which explains the strong negative correlation between the two.
Examination of circulation and precipitation variations associated with the
PDO in a longer time period (either from 1979 to 2015 or 1948 to 2015) show
generally similar patterns, but also with some discrepancies. If the
conditions associated with high DOD in Syria are valid beyond the recent
decade, i.e., 2003–2015, the negative role of the PDO on spring dust
activities in Syria is also likely to persist.
Analysis on strong dust storms in spring
Severe dust storms usually only persist a few days or even a few hours (e.g.,
Haboobs), and seasonal or monthly averages reduce the variability of dust
activities and may smooth out some important features. In this section we
discuss the conditions associated with strong dust storms in Syria, using
daily DOD and reanalysis variables, and compare these conditions with those
from seasonal mean patterns discussed above including the teleconnections
with the PDO. Composites are formed based on daily Syrian DOD index from
Aqua. Days during March, April, and May from 2003 to 2015 are selected when
the daily anomaly (with reference to long-term mean) of DOD index is greater
than 1 standard deviation to form daily composites. Results are very similar
but patterns are in slightly stronger (weaker) magnitudes if choosing the
threshold of 1.5 (0.5) standard deviations (not shown).
Composites of (a) Aqua and (b) Terra daily DOD
(shading) along with ERA-Interim 10 m horizontal wind anomalies (with
reference to the 1979–2015 mean; vectors) for days with Syrian DOD index
(Aqua) greater than 1 standard deviation during MAM from 2003 to 2015.
Shading shows values significant at the 95 % confidence level over land,
while wind vectors significant at the 95 % confidence are plotted in blue
(t test).
Figure 10 shows the composite of Aqua and Terra daily DOD (shading) and
ERA-Interim 10 m winds based on the Aqua DOD index. The patterns are quite
similar in Aqua and Terra DODs. DOD anomaly is above 0.3 over Syria and
western Iraq (Fig. 10a–b). Anomalous high DOD is also located over eastern
Saudi Arabia and the northeastern Africa, indicating a possible transport of
dust from these areas to Syria. Anomalous cyclonic flow is centered over
southern Turkey around 30∘ E, with anomalous strong westerly wind
blowing from North Africa and southerly flow from the eastern Arabian
Peninsula, consistent with dust transport discussed in the above section.
However, different from the seasonal mean regression patterns (e.g., Fig. 6),
there are the anomalous southerly winds from the Red Sea and Persian Gulf.
Figure 11 shows the composite of daily precipitation from the ERA-Interim
twice-daily forecast and TRMM daily precipitation for days with DOD anomaly
above 1 standard deviation. Precipitation anomalies are shown in percentages
(with reference to the climatological mean) instead of absolute values in
order to highlight the precipitation variations over the Middle East, where
the magnitude of precipitation in spring is quite low (less than
1 mm day-1 in most of the areas). Patterns of precipitation anomalies
associated with strong dust storms are quite similar in the ERA-Interim and
TRMM. Precipitation increases significantly (more than 80 %) over Turkey
and the northeastern Mediterranean, but decreases over the central and
southern Arabian Peninsula and northeastern Africa. Syria sits in between the
anomalous wet and dry regions, with slightly increased precipitation in its
northern domain. These features are somewhat similar to the results of
previous studies on strong dust storms and our understanding on the seasonal
mean patterns associated with high DOD in Syria. Strong dust storms such as
Haboobs (usually about 1 km height and tens to hundreds of kilometers in
length) are usually associated with convective storms (e.g., Miller et al.,
2008; Roberts and Knippertz, 2012; Vukovic et al., 2014; Dempsey, 2014). The
cold downdraughts from convective storms spread out and can lift the dust
from the surface to form a dusty towering “wall” as the front of a Haboob.
Similarly, severe precipitation and convection in Turkey and northern Syria
can produce an unstable atmospheric condition in the region, and the
intensified low-level winds can lift dust from the surface and thus increase
DOD. Reduced precipitation over the southern Arabian Peninsula and North
Africa facilitates dust transport from these source areas to Syria.
Composites of daily precipitation anomalies (shading; % with
references to the climatology) from (a) the ERA-Interim and
(b) TRMM for the days with Syrian DOD index (Aqua) greater than 1
standard deviation during MAM from 2003 to 2015. Areas significant at the
95 % confidence level (t test) are dotted.
Composites of (a) vertically integrated mass-weighted
moisture flux (vectors; kg m-1 s-1) and its magnitude (shading)
from the NCEP1 and (b) CAPE (10 J kg-1) along with 850 hPa
winds (m s-1) from the ERA-Interim for the days with Syrian DOD index
(Aqua) greater than 1 standard deviation during MAM from 2003 to 2015.
Moisture flux is integrated from surface to 300 hPa. Shading areas
significant at the 95 % confidence level are dotted.
Figure 12 shows composites for a vertically integrated mass weighted moisture
flux (vectors) and its magnitude (shading) from the NCEP1 and convective
available potential energy (CAPE) from the ERA-Interim for days with Aqua DOD
anomaly greater than 1 standard deviation. Figure 12a shows an anomalous
westerly flux from northern Egypt and a southerly flux from the Red Sea and
Persian Gulf largely increase the moisture transport to Syria and eastern
Turkey, while the reduced moisture fluxes along the south coast of Iran,
southern Arabian Peninsula, southern Red Sea, and the Gulf of Aden are quite
similar to those patterns associated with high Syrian DOD in spring (Fig. 8b
and d) and a negative PDO (i.e., opposite to Fig. 8c). Consistently, CAPE is
increased over Turkey and Syria, indicating an unstable atmospheric condition
associated with increased moisture transport to the region, while over the
southern Arabian Peninsula and northeastern Africa where moisture flux is
reduced, CAPE is decreased (Fig. 12b).
The connection between the PDO and daily strong dust storms is also verified
by correlating an index of the occurrence of strong dusty events (i.e., daily
DOD anomaly greater than 1 standard deviation) in MAM with HadISST from 2003
to 2015 (Fig. S5).
Figures 10–12 suggest that severe dust storms occur under both favorable
large-scale and regional-scale features. Remote forcing such as PDO modifies
springtime circulation and precipitation patterns. For example, a negative phase of
PDO decreases precipitation over the southern Arabian Peninsula and northeastern
Africa and favors the transport of dry dusty air from these regions to Syria,
while strong convective storms over Turkey favor the dust lifting and the
formation of strong dust storms in Syria.
Can the recent GFDL climate model capture the connection between
the PDO and Syrian DOD?
(a) Correlation between AM3 DOD index averaged over Syria
(see Fig. 2) and surface temperature from 1960 to 2010 and
(b) regression of 850 hPa (shading) and 200 hPa (contours; solid
lines for positive values and dashed lines for negative values, from -20 to
20 gpm with intervals of 5 gpm, zero line is not shown) geopotential height
onto the standardized DOD index from 1960 to 2010. Shading areas significant
at the 95 % confidence level are dotted.
To what extent can current climate model capture the connection between the
PDO and Syrian DOD? We examined such relationships in the GFDL AM3 model.
Figure 13a shows the correlation between a modeled DOD index and surface
temperature from 1960 to 2010 in MAM. The correlation pattern over the North
Pacific is quite similar to that of a negative PDO (e.g., Fig. 3c), but only
significant over the northern North Pacific, indicting a weaker such
connection in the model. Over land, DOD is highly positively associated with
surface temperature in northern to northeastern Africa, which is not seen in
the observations, and may suggest an overestimation of the connection in the
model. The correlation pattern is similar if calculated from 1951 to 2010,
but with a slightly stronger positive correlation over the tropical eastern
Pacific (not shown).
Figure 13b shows the regression of standardized DOD index onto 200 hPa
(contours) and 850 hPa geopotential heights (shading) during MAM 1960–2010.
The wave trains propagation from the north Pacific to the Middle East is
quite similar to that shown in the NCEP1 using the observed DOD index
(Fig. 4b and d) but in a weaker magnitude in the tropical and subtropical
North Pacific, consistent with the weak SST correlations (Fig. 13a). The
anomalous low over Africa dips down to the eastern Sahel, probably in
association with the biased correlation between the DOD and surface
temperature over northeastern Africa.
Figure 13 suggests that AM3 can partially capture the connection between the
dust activities in Syria and the PDO during 1960–2010. This complements our
satellite observation-based analysis on their relationship in the recent
decade, although the modeled relationship is weaker than that in the
observations. A few reasons may contribute to this underestimation. Firstly,
AOD is slightly underestimated in the Middle East in the model compared with
AERONET, and the simulated DOD is also less than that in MODIS, which
indicates that dust variability may be underestimated in the region.
Secondly, the current dust scheme in AM3 only relates dust emission to dust
source map and surface wind speed, while the influence of soil moisture on
dust emission is not explicitly considered. Thus, the anomalous dust
transport from southern Arabian Peninsula and northeast Africa in association
with the dry conditions under a negative PDO may not be fully captured by the
model. Our analysis also suggests that the DOD in the model is highly
correlated with dry deposition over the western Mediterranean, along the
north coast of Egypt, and over Turkey and is also correlated with the
southwesterly winds in this region, indicating a very strong connection with
the dust sources in Africa, which may be an
overestimation. However, it
is not uncommon for global or regional climate models to have difficulties
capturing the interannual to decadal variations of dust aerosols (e.g., Evan
et al., 2014; Solmon et al., 2015). This is why we did not choose the climate
model as a major tool to examine the connection between the PDO and Syrian
DOD in this work. A new dust emission scheme that considers the influences of
soil moisture and vegetation cover and land use changes is currently under
development, and the relationship between Syrian DOD and PDO is likely to be
better represented in this newer version of the GFDL model.
Conclusions
Dust activities in the Middle East have been related to many factors, such as
remote sea surface temperatures, near-surface winds, vegetation coverage, and
precipitation variability. The ongoing civil war and a recent severe dust
storm in Syria in 2015 raised concerns as to whether dust activities will
increase in the region. The first step toward answering this question is to
understand the dust activities driven by the natural climate variability.
Here we examine the connection between Syrian dust activities and the Pacific
Decadal Oscillation using innovative dust optical depth datasets retrieved
from MODIS Deep Blue aerosol products and multiple observations and
reanalyses.
A significantly negative correlation is found between Syrian DOD and the PDO
in springtime during 2003–2015, suggesting that the PDO index explains about
81 % variances of Syrian DOD in spring in the recent decade. Such a
connection is revealed not only by precipitation as emphasized by previous
studies (e.g., Yu et al., 2014; Notaro et al., 2015) but also on other
aspects such as the circulation patterns and surface winds. It is found that
high DOD in Syria during spring is associated with low geopotential height
over Europe, the southern Arabian Peninsula, and northeastern to eastern
Africa. Associated with these anomalous height patterns are the westerly wind
anomalies over the Mediterranean basin and southerly wind anomalies over the
southeastern Arabian Peninsula, favoring dust transport from these regions to
Syria, where the transported dust dominates the DOD in spring (Ginoux,
2015). A positive PDO is connected with wind and height patterns largely
opposite to those associated with high DOD over Syria. The positive phase of
the PDO also tends to increase precipitation over the Arabian Peninsula and
northeastern Africa via anomalous moisture transport that increases moisture
supply and also reduces the stability of low-level atmosphere. A negative PDO
thus is not only associated with wind and geopotential height patterns
favorable to high DOD in Syria but also tends to reduce precipitation in the
dust source regions such as Iraq, Saudi Arabia, and northeastern Africa, and
thus favors dust transport to Syria. This explains why the correlation
between the Syrian DOD index and the PDO index is much higher than other
individual index such as precipitation, leaf area index, and 10 m winds in
Syria (Tables 1–2). The influences of the PDO on circulation and
precipitation patterns over the Middle East largely persist beyond the recent
decade, e.g., over 1948–2015, but also show some exceptions. The lack of
long-term observations also brings uncertainties to the connection between
the PDO and Syrian DOD.
Unlike the patterns on seasonal mean discussed above, analysis on the daily
composites of strong spring dust storms shows the influence of both the PDO
and local features. In spring, strong dust storms (DOD anomalies greater than
1 standard deviation) in Syria are associated with an anomalous cyclonic flow
centered over the northeastern Mediterranean Sea and Turkey, and southerly
wind anomalies from the Red Sea and Persian Gulf. Consistently, moisture flux
onto Turkey and Syria is enhanced and thus destabilizes the atmosphere and
promotes precipitation in Turkey and convection and dust uplifting in Syria.
Meanwhile, reduced moisture fluxes onto the southern Arabian Peninsula, east
coast of Egypt, and the Republic of the Sudan (in association with a negative PDO) favor a dry
and stable condition in Saudi Arabia and northeastern Africa, facilitating
dust transport from these regions to Syria.
We examined the teleconnection between Syrian DOD and the PDO in the GFDL AM3
model. A weaker connection compared to that in the observation is found,
which may be partially related to the model's underestimation of the mean DOD
and its variability in this area. The new dust scheme that includes the
influence of soil moisture and precipitation is likely to overcome these
drawbacks and provide a better representation of the relationship between
Syrian DOD and the PDO.
Data availability
PRECL Precipitation data are provided by the NOAA/OAR/ESRL PSD, Boulder,
Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. PDO
and Niño 3.4 indices are downloaded from the website of NOAA Climate
Prediction Center
(http://www.esrl.noaa.gov/psd/data/climateindices/list/). The NCEP/NCAR
reanalysis product was obtained from
http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html, and
the ERA-Interim is downloaded from
http://www.ecmwf.int/en/research/climate-reanalysis/era-interim.
HadISST is downloaded from
http://www.metoffice.gov.uk/hadobs/hadisst/data/download.html while CRU
TS 3.23 temperature and precipitation are downloaded from
https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/cruts.1506241137.v3.23/.
The MODIS Deep Blue aerosol products were acquired from the Level-1 and
Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive
Center (DAAC), located in the Goddard Space Flight Center in Greenbelt,
Maryland (https://ladsweb.nascom.nasa.gov/).
The Supplement related to this article is available online at doi:10.5194/acp-16-13431-2016-supplement.
Acknowledgements
This research was
supported by NOAA, Princeton University's Cooperative Institute for Climate
Science, and NASA under grants NNG14HH42I-MAP and NNH14ZDA001N-ACMAP.
Comments and suggestions from the reviewers improved the paper and are
gratefully appreciated.Edited by:
E. Gerasopoulos Reviewed by: two anonymous referees
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