These authors contributed equally to this work.
Previous studies have noted a relationship between African dust and Atlantic tropical cyclone (TC) activity. However, due to the limitations of past dust analyses, the strength of this relationship remains uncertain. The emergence of aerosol reanalyses, including the Navy Aerosol Analysis and Prediction System (NAAPS) aerosol optical depth (AOD) reanalysis, NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and ECMWF Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), enables an investigation of the relationship between African dust and TC activity over the tropical Atlantic and Caribbean in a consistent temporal and spatial manner for 2003–2018. Although June–July–August (JJA) 550 nm dust AOD (DAOD) from all three reanalysis products correlates significantly over the tropical Atlantic and Caribbean, the difference in DAOD magnitude between products can be as large as 60 % over the Caribbean and 20 % over the tropical North Atlantic. Based on the three individual reanalyses, we have created an aerosol multi-reanalysis consensus (MRC). The MRC presents overall better root mean square error over the tropical Atlantic and Caribbean compared to individual reanalyses when verified with ground-based AErosol RObotic NETwork (AERONET) AOD measurements. Each of the three individual reanalyses and the MRC have significant negative correlations between JJA Caribbean DAOD and seasonal Atlantic accumulated cyclone energy (ACE), while the correlation between JJA tropical North Atlantic DAOD and seasonal ACE is weaker. Possible reasons for this regional difference are provided. A composite analysis of 3 high-JJA-Caribbean-DAOD years versus 3 low-JJA-Caribbean-DAOD years reveals large differences in overall Atlantic TC activity. We also show that JJA Caribbean DAOD is significantly correlated with large-scale fields associated with variability in interannual Atlantic TC activity including zonal wind shear, mid-level moisture, and sea surface temperature (SST), as well as the El Niño–Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), implying confounding effects of these factors on the dust–TC relationship. We find that seasonal Atlantic DAOD and the AMM, the leading mode of coupled Atlantic variability, are inversely related and intertwined in the dust–TC relationship. Overall, DAOD in both the tropical Atlantic and Caribbean is negatively correlated with Atlantic hurricane frequency and intensity, with stronger correlations in the Caribbean than farther east in the tropical North Atlantic.
Saharan dust particles can affect weather and climate through both direct and indirect radiative and cloud processes, notably in association with boreal summer Saharan Air Layer (SAL) outbreaks. The SAL is a layer of hot and dry air that forms over continental West Africa and is then advected over the low-level moist marine boundary layer of the tropical Atlantic (Carlson and Prospero, 1972). The SAL is often associated with the African easterly jet (AEJ), which can enhance vertical wind shear. Despite numerous observational and modeling studies that have examined the relationships between these aspects of the SAL and Atlantic tropical cyclone (TC) activity, there are conflicting findings as to whether dust acts to generally inhibit or enhance tropical cyclogenesis and intensification. Some studies suggest negative impacts of the SAL's dust-laden dry air and the AEJ on TC activity (e.g., Dunion and Velden, 2004; Lau and Kim, 2007; Jones et al., 2007; Sun et al., 2008; Pratt and Evans, 2008), while others have focused exclusively on the dust particles themselves and have found a negative influence on TCs (e.g., Evan et al., 2006a; Rosenfield et al., 2007; Strong et al., 2018; Reed et al., 2019). Other studies have suggested little impact of the SAL on TCs (e.g., Braun, 2010; Sippel et al., 2011; Braun et al., 2013), while others have posited a positive impact of dust on TCs through cloud-microphysical processes (e.g., Jenkins et al. 2008). Finally, others have suggested that there are contrasting influences through different mechanisms and for different TCs (Karyampudi and Pierce, 2002; Bretl et al. 2015; Pan et al., 2018), highlighting the complexity of the dust–TC interaction.
African dust impacts the North Atlantic throughout the year, with its summer peak season (May–August) overlapping and leading the peak of the Atlantic hurricane season (August–October; Fig. S1). As African dust outbreaks during the summer are often associated with the SAL, airborne dust has often been used as an indicator for the SAL (Dunion and Velden, 2004; Dunion, 2011; Tsamalis et al., 2013), although early season cases where the majority of the dust existed in the marine boundary layer below the trade wind inversion instead of staying aloft were also found (Reid et al., 2003). Saharan dust and the SAL are frequently observed throughout the Caribbean and as far west as Central America and the North American continent during the boreal summer (e.g., Prospero, 1999; Reid et al., 2003; Dunion and Velden 2004; Nowottnick et al., 2011; Kuciauskas et al., 2018). Airborne dust associated with the SAL often extends to 5.5 km (500 hPa) off of western Africa and becomes thinner as its top lowers and its base rises as it is advected westward, shrinking to below 2 km in the Caribbean and in the Gulf of Mexico (Tsamalis et al., 2013). In some strong SAL cases, however, the top of the dust layer can reach 6 km (Reid et al., 2003; Colarco et al., 2003). During their trans-Atlantic transport, dust aerosols are from time to time observed to interact with TCs, as seen in satellite imagery (Fig. 1).
True-color Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery composited on 12
September 2012 and overlaid with NAAPS-RA 550 nm dust aerosol optical depth
(DAOD, an approximate measure of total atmospheric column of dust aerosol
mass, unitless) isopleths, showing Hurricane Nadine's interaction with the
SAL. Hurricane Nadine is located in the middle of the image. African dust appears as a
light transparent brown haze in between the African coast and Hurricane Nadine, as well as
wrapping around the northern periphery of the storm. Note that areas of
sunglint (narrow regions between the light blue curves) are similar in color
to the dust aerosols but have the same orientation as the satellite orbits
and are located approximately midway between satellite coverage gaps (black
regions oriented south-southwest to north-northeast). Satellite imagery
courtesy of the MODIS flying
on NASA's Terra satellite and available from
African dust and its associated SAL has been hypothesized to impact TCs through a variety of mechanisms. Through scattering and absorbing sunlight, dust reduces solar radiation reaching the surface, thus cooling sea surface temperatures (SSTs; e.g., Miller and Tegen, 1998; Lau and Kim, 2007; Evan et al., 2009). Lower SSTs provide TCs with less energy to initiate, develop, and maintain strength. Through additional radiative heating of the dusty layer, mineral dust is also suggested to impact the structure, location, and energetics of the AEJ (Tompkins et al., 2005; Wilcox et al., 2010; Reale et al., 2011) and African easterly wave (AEW) activity (Karyampudi and Carlson, 1988; Reale et al., 2009; Nathan et al., 2017; Jones et al., 2004; Ma et al., 2012; Grogan et al., 2016, 2017; Bercos-Hickey et al., 2017), thus having implications for tropical cyclogenesis. From a thermodynamic point of view, Dunion and Velden (2004) have proposed that the dust-carrying SAL outbreaks could inhibit TC formation and development in the North Atlantic through three primary mechanisms, including dry air intrusion into the storm, enhancement of the local vertical wind shear associated with the enhanced AEJ, and stabilization of the environment due to radiative heating of the dust layer above the marine boundary layer.
Dust particles can also act as cloud condensation nuclei (Twohy et al., 2009; Karydis et al., 2011) and ice nuclei (DeMott et al., 2003; Sassen et al., 2003) and affect cloud microphysics, weakening or strengthening convection depending on the environment (Khain, 2009). Focusing specifically on TCs, there is not a consistent conclusion among studies on whether the microphysical impacts of dust weaken or strengthen TCs (Jenkins et al., 2008; Rosenfeld et al., 2007; Zhang et al., 2007, 2009; Herbener et al., 2014; Nowottnick et al., 2018).
While dust aerosols can affect TC formation and development through radiative and cloud-microphysical impacts, TCs can in turn impact dust aerosol spatial distributions through wet removal and dynamic flow (Herbener et al., 2016). AEWs, serving as seeding disturbances for TCs (Landsea, 1993), are shown to contribute to dust emission and transport (e.g., Westphal et al., 1987; Jones et al., 2003; Knippertz and Todd, 2010). Climate variability that affects TC activity can also impact African dust emission and transport over the North Atlantic and Caribbean. For example, the El Niño–Southern Oscillation (ENSO) was found to affect the emission and transport of African dust as well (Prospero and Lamb, 2003; DeFlorio et al., 2016), especially during the boreal winter (Prospero and Nees, 1986; Evan et al., 2006b).
How all of these factors interact in the complex climate system and to what extent they can impact TC formation and intensification is still largely unknown. The goal of this study is to explore how the integrated interactions manifest themselves in the relationship between Saharan dust and Atlantic TC activity on seasonal to interannual timescales using state-of-the-art aerosol reanalysis data. This serves as a first step towards further understanding the dust–TC relationship and evaluating the relative importance of different mechanisms. Previous empirical studies on the relationship between African dust and Atlantic TC activity are limited by uneven spatial and temporal sampling by satellite and in situ-based observations. The emergence of several aerosol reanalysis datasets, including the Navy Aerosol Analysis and Prediction System (NAAPS) aerosol optical depth (AOD) reanalysis (NAAPS-RA, Lynch et al., 2016), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) aerosol reanalysis (Randles et al., 2017), and the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) (Inness et al., 2019), allow us to investigate this relationship in a more consistent manner over their joint time period to provide a degree of statistical robustness.
In Sect. 2, an introduction to the aerosol and large-scale environmental data and the analysis methods employed is provided. Section 3 presents the dust AOD (DAOD) climatology, its interannual variability over the Atlantic, and comparisons of the three aerosol reanalyses. This section also evaluates correlations between DAOD and Atlantic TC activity, as well as the relationship between DAOD and large-scale environmental conditions and climate modes. The sensitivity of the results to the definition of the regions, the number of composite years used, and the definition of dust seasons are provided in Sect. 4. A discussion and conclusions are given in Sect. 5.
Regardless of the underlying mechanisms, as there are contradicting mechanisms proposed in different studies, the goal of this study is to examine if there is a robust and statistically significant relationship between African dust and Atlantic TC activity on seasonal to interannual timescales. We also examine if there are confounding factors, for example, meteorological conditions and climate modes, that covary with dust and hence influence TC activity.
We use dust AOD (DAOD) to represent Atlantic dust levels. Three aerosol reanalysis products, and their consensus DAOD, are used in order to increase the fidelity of the analysis result, given that multi-model consensus typically has been shown to have better data quality in prior assessments (Sessions et al., 2015; Xian et al., 2019). Various TC count indices and accumulated cyclone energy (ACE) (Bell et al. 2000), defined in the next section, are utilized to represent TC activity.
The Atlantic Main Development Region (MDR) (e.g., Goldenberg et al., 2001),
including the Caribbean (10–20
Climatological (2003–2018 average) monthly mean DAOD (left column)
and the ratio of DAOD to total AOD (right column) for June–October based on
the MRC. The middle column shows the climatological 700 hPa RH (color
shading) and horizontal wind vectors from ERA-Interim. Black boxes denote
the MDR, including the Caribbean (left, 10–20
The correlations between variables of interest are based on the Pearson
correlation coefficient. Statistical significance is assessed at the 95 %
level using a two-tailed Student
A combination of aerosol reanalyses are used to describe the aerosol environment over the tropical North Atlantic and Caribbean. An aerosol multi-reanalysis consensus (MRC) based on three aerosol reanalysis products, including the NAAPS-RA (Lynch et al., 2016) from the US Naval Research Laboratory, MERRA-2 (Randles et al., 2017) from NASA, and CAMSRA (Inness et al., 2019) from ECMWF, are also generated and used. The analysis period is focused on 2003–2018, when all three aerosol reanalyses are available and both Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) AOD retrievals were assimilated therein.
The NAAPS-RA product provides 550 nm speciated AOD at a global scale with
As part of the upgrade from the original MERRA reanalysis (Rienecker et al.,
2011) based on the Goddard Earth Observing System (GEOS) Earth system model,
MERRA-2 now incorporates assimilation of AOD from a variety of remote
sensing sources, including AERONET, MODIS, and MISR after 2000, as well as the Advanced Very High Resolution Radiometer (AVHRR)
before 2002. The aerosol module used for MERRA-2 is the Goddard Chemistry,
Aerosol, Radiation, and Transport model (GOCART; Chin et al., 2000; Colarco
et al., 2010), which provides simulations of dust, sea salt, black and
organic carbon, and sulfate aerosols, and is run radiatively coupled to the
GEOS Atmospheric Global Climate Model (AGCM). A detailed description and validation of the AOD reanalysis
product can be found in Randles et al. (2017) and Buchard et al. (2017). For
the purpose of this study, monthly mean DAOD at 550 nm with 0.5
Root mean square error of total AOD (left number in each cell) and
coarse-mode AOD (right number in each cell) at 550 nm from individual aerosol
reanalyses, including CAMSRA, MERRA-2, NAAPS-RA, and the
multi-reanalysis consensus (MRC) verified with AERONET V3L2 monthly data for
the 2003–2018 time period. The rank of MRC among all reanalyses in terms of
root mean square error (RMSE) is also shown. “
Under the banner of the Copernicus Atmosphere Monitoring Service (CAMS),
operated by ECMWF on behalf of the European Commission, a new global
reanalysis of atmospheric composition has been produced: CAMSRA (Inness et
al., 2019). This is the successor to the MACC reanalysis (Inness et al.,
2013) and CAMS interim reanalysis (Flemming et al., 2017) produced
previously at ECMWF. The dataset spans the period 2003–2018 and is being
continued for subsequent years. The model component is based on the same
Integrated Forecasting System (IFS) used at ECMWF for weather forecasting
and meteorological reanalysis but at a coarser resolution and with
additional modules activated for prognostic aerosol species (dust, sea salt,
organic matter, black carbon, and sulfate) and trace gases. The impact of
the aerosols (and ozone) on radiation and thereby on meteorology is included
in the model. For aerosols, observations of total AOD at 550 nm are
assimilated from MODIS (Terra and Aqua) for the whole period and from the
Advanced Along-Track Scanning Radiometer for 2003–2012, using a 4D
variational data assimilation system with a 12 h data assimilation window
along with meteorological and trace gas observations. The speciated AOD
products used in this study are available at a 3-hourly temporal resolution
and a
Based on the three aerosol reanalysis products described above, we made a
MRC product following the multi-model-ensemble method of the International
Cooperative for Aerosol Prediction (ICAP, Sessions et al., 2015; Xian et
al., 2019). The MRC is a consensus mean of the three individual reanalyses,
with a
AERONET is a ground-based global-scale sun photometer network that includes instruments to measure sun and sky radiance at wavelengths ranging from the near ultraviolet to the near infrared during daytime hours. This network has been providing high-accuracy and high-quality measurements of aerosol properties since the 1990s (Holben et al., 1998; Holben et al., 2001) and is often used as the primary dataset for validating aerosol optical properties in satellite retrievals and model simulations (e.g., Levy et al., 2010; Colarco et al., 2010; Kahn and Gaitley, 2015). Only cloud-screened, quality-assured version 3 Level 2 AERONET data are utilized in this study (Giles et al., 2019). AERONET multiple wavelength measurements were used to derive both fine- and coarse-mode AODs at 550 nm based on the spectral deconvolution method (SDA) of O'Neill et al. (2003). The SDA product was verified with in situ measurements (Kaku et al., 2014) and was shown to be able to capture the full modal properties of fine and coarse particles. Temporally, AERONET data are averaged into 6 h bins centered at the regular model output times of 00:00, 06:00, 12:00, and 18:00 UTC. Monthly mean AERONET AOD is derived only when the total number of 6 h AERONET data is greater than 18 to ensure temporal representativeness.
Atlantic basin TC data were taken from the Atlantic hurricane database version 2 (HURDAT2; Landsea and Franklin, 2013). This dataset contains 6-hourly information (including position, maximum sustained winds, and central pressure – where available) for every TC observed in the Atlantic basin dating back to 1851.
The ERA-Interim reanalysis (Dee et al., 2011) is a global atmospheric reanalysis produced by the ECMWF that uses a four-dimensional variational analysis with a 12 h analysis window. The spectral resolution of these data is approximately 80 km (T255), is available on 60 vertical levels from the surface to 0.1 hPa, and is available from January 1979 to August 2019. We use monthly mean large-scale fields, including vector wind, atmospheric temperature, and relative humidity data on several pressure levels.
The National Oceanic and Atmospheric Administration (NOAA) Optimum
Interpolation (OI) SST product (Reynolds et al., 2002) is utilized for SST
calculations. NOAA OI SST v2 uses a combination of in situ data, satellite
data, SSTs simulated by sea-ice cover, and bias adjustments to arrive at its
final estimate of SSTs. NOAA OI SST v2 data are available on a
The Oceanic Niño Index (ONI), defined to be a 3-month average of the
Niño 3.4 (5
The SST component of the AMM (Kossin and Vimont, 2007) is investigated to assess the relationship between DAOD and tropical Atlantic oceanic conditions. While the index is not standardized, we have standardized it by its 1981–2010 average and standard deviation.
The genesis potential index (GPI) was calculated using monthly-averaged ERA-Interim data following Emanuel and Nolan (2004). The maximum potential intensity (MPI) was calculated using monthly-averaged ERA-Interim temperature and moisture and NOAA OI SST following Bister and Emanuel (2002).
Figure 2 shows the MRC monthly DAOD climatology based on the 2003–2018
average as well as the ratio of DAOD to total AOD for June–October over the
tropical Atlantic. Climatologically from June to October, the majority of
airborne dust originates from the Sahara Desert, in contrast to the winter
season when a significant amount of dust is emitted over the Sahel and
southern Sahara (Engelstaedter and Washington, 2007). This dust is then
transported westward over the Atlantic and eventually to the Caribbean,
largely within the 10–25
As transport of Saharan dust across the Atlantic during summer is often
associated with SAL outbreaks, which are approximately centered around 700 hPa (Dunion and Velden, 2004; Dunion, 2011), monthly climatological 700 hPa
relative humidity (RH) and horizontal wind are also shown in Fig. 2.
Climatologically, the MDR is dominated by the mid-level easterly jet (7–8 m s
Monthly mean version 3 L2 AERONET and MRC 550 nm modal AODs at four
African dust-impacted sites: Dakar, Cabo Verde, Ragged Point, and La Parguera
from east to west. JJA are highlighted with pink shading, and JJA seasonal
average total AODs from MRC are shown with red bars. Annotations for each
time series show bias, RMSE, and correlation (
Figure 3 shows the monthly mean AERONET version 3 L2 and MRC 550 nm modal
AOD time series at four AERONET sites that are primarily influenced by
African dust. From east to west, these sites include Dakar, Senegal
(14.4
Figure 3 also shows the bias, the root mean square error (RMSE) of MRC, and
the correlation (
Monthly DAOD at 550 nm from CAMSRA, MERRA-2, NAAPS-RA, and MRC from 2003 to 2018 for the
Figure 4 shows the time series of monthly mean and regionally averaged DAOD
from MRC and the three contributing reanalyses from 2003 to 2018 for the
tropical North Atlantic and Caribbean as defined in Fig. 2. The DAODs from
the three reanalyses have similar seasonal and interannual variability and
are highly correlated, with
Accumulated cyclone energy (ACE) is often utilized to represent TC activity
and is defined to be the square of the 1 min maximum sustained wind
speed at each 6-hourly interval when a tropical or subtropical cyclone
(with maximum sustained winds
Annual average Atlantic TC activity in the three seasons with the highest JJA Caribbean DAOD (2014, 2015, and 2018) and the three seasons with the lowest JJA Caribbean DAOD (2005, 2011 and 2017). Ratios between the three low-DAOD seasons and the three high-DAOD seasons are also provided. Corresponding numbers for the Caribbean are provided in parentheses next to the total basin-wide numbers.
Atlantic TC activity shows a statistically significant relationship with
regionally averaged Caribbean JJA DAOD. Figure 5a displays this
relationship, with higher Caribbean DAOD correlating (
Scatterplot showing the relationship between annual Atlantic
accumulated cyclone energy (ACE) and JJA region-averaged DAOD from MRC,
NAAPS-RA, MERRA-2, and CAMSRA over the
Given the strength of the relationship between Caribbean DAOD and seasonal Atlantic ACE, we next investigate the relationship in extreme JJA DAOD seasons. We take the three seasons from 2003 to 2018 when JJA Caribbean DAOD was at its highest levels and when it was at its lowest levels.
The three seasons with the highest JJA Caribbean DAOD were 2018, 2015, and
2014 in descending order, and the three seasons with the lowest JJA
Caribbean DAOD were 2005, 2011, and 2017 in ascending order based on MRC. The
left column of Fig. 6 shows DAOD composites for the three high-JJA-Caribbean-DAOD seasons and the
three low-JJA-Caribbean-DAOD seasons and their differences. DAOD is not only
higher over the MDR in the high-Caribbean-DAOD seasons, but dust aerosols
are also transported farther to the west. DAOD differences between the extreme
high- and low-DAOD seasons over the tropical North Atlantic and Caribbean are
The right column of Fig. 6 displays the JJA-averaged 850 hPa winds and 700 hPa RH for the three high-JJA-Caribbean-DAOD seasons and three low-JJA-Caribbean-DAOD seasons, as well as the
difference between these high- and low-DAOD seasons. Large-scale conditions over
the Caribbean during JJA were much less conducive for TCs in the high-DAOD
seasons, with drier middle levels (> 10 % relative humidity
difference) and stronger easterly trade winds (2–4 m s
Table 2 displays observed Atlantic TC activity as well as the ratios of
observed average seasonal Atlantic TC activity in the three low-JJA-Caribbean-DAOD seasons versus the three high-Caribbean-DAOD seasons.
Atlantic basin-wide numbers of tropical depressions, named storms,
hurricanes, major (Category 3
In addition, the ratio for major hurricanes is higher than the ratios for tropical depressions, named storms and hurricanes, indicating a stronger relationship between dust aerosols and intense storms than between dust aerosols and weak storms. A total of 17 major hurricanes were observed in the Atlantic in the three low-Caribbean-DAOD seasons, compared with only 6 major hurricanes in the three high-Caribbean-DAOD seasons. The three low-Caribbean-DAOD seasons had six continental United States major hurricane landfalls (2005 hurricanes Dennis, Katrina, Rita, and Wilma and 2017 hurricanes Harvey and Irma), while the three high-Caribbean-DAOD seasons had one continental United States major hurricane landfall (2018 Hurricane Michael).
Formation locations of Atlantic named storms during the three
seasons with
Figure 7 displays the named storm formation location of all Atlantic TCs in
the three seasons with the highest JJA Caribbean DAOD and the three seasons
with the lowest Caribbean DAOD along with the maximum intensity that these
TCs reached. As would be expected from the differences in large-scale
conditions noted earlier, TCs that became major hurricanes formed much more
frequently south of 20
We next examine the relationship between JJA DAOD and large-scale
atmosphere/ocean fields. In this analysis, we begin by focusing on several
fields that have been documented in prior research to significantly impact
Atlantic TC activity: 850 hPa zonal wind (850 hPa
Correlation between MRC JJA regionally averaged DAOD in the
Caribbean and JJA
Figure 8 displays the correlation between regionally averaged MRC JJA DAOD
in the Caribbean and the six large-scale fields just discussed. Higher JJA
Caribbean DAOD is associated with stronger 850 hPa easterly trades and
increased 200 hPa upper-level westerlies (and hence stronger vertical wind
shear), drier air at 700 hPa, and anomalously cool SST across the MDR. Weaker
850 hPa relative vorticity also predominates over most of the Caribbean.
However, almost no correlation is found between Caribbean JJA DAOD and 850 hPa relative vorticity in the tropical North Atlantic, possibly due to the
counteracting role of covariability of African dust emissions and AEWs – as
was inferred from a positive correlation between the two by Karyampudi and
Carlson (1988). This result is also consistent with Fig. 7, which shows more named storms and therefore likely stronger AEW activity right off of
the coast of west Africa between 10 and 20
The relationship between the same six large-scale fields and JJA tropical North Atlantic DAOD is considerably weaker, with lower correlations observed for all six fields (Fig. S2). In addition, the regions with significant correlations decrease in spatial extent relative to the Caribbean DAOD correlations shown in Fig. 8.
Correlation between
We next investigate maximum potential intensity (MPI), an integrated TC
index, which combines a list of key factors (similar to those explored
above). MPI assesses how conducive atmospheric thermodynamic conditions are
for TC intensification, providing a theoretical limit of the strength of a
TC (Holland, 1997; Bister and Emanuel, 1998). Figure 9a and c show the
correlations between the Caribbean region-averaged JJA DAOD and JJA and ASO (August, September, and October) MPI
calculated based on Bister and Emanuel (1998). Consistent with the results
for the individual large-scale fields, JJA MPI over the MDR exhibits strong
negative correlations with JJA Caribbean DAOD (
Correlation matrix between regionally averaged
multi-reanalysis consensus (MRC) JJA tropical North Atlantic/Caribbean DAOD
and 850 hPa
The genesis potential index (GPI) is another integrated TC index that is
often used to provide an estimate of the potential for tropical cyclogenesis
(e.g., Emanuel and Nolan, 2004; Camargo et al., 2007). Monthly GPI is
calculated following Emanuel and Nolan (2004). Figure 10 shows the
correlation between region-averaged JJA DAODs and JJA and ASO GPI.
Consistent with the results for the individual large-scale fields and MPI,
JJA GPI over the MDR exhibits strong negative correlations (
As in Fig. 9 but for the genesis potential index (GPI).
Table 3 summarizes the relationship between large-scale atmosphere/ocean fields, MPI, GPI, and DAOD, with correlations displayed between JJA region-averaged DAOD and concurrent region-averaged fields (i.e., JJA-averaged), as well as the large-scale region-averaged fields during the peak of the Atlantic hurricane season from August-October. The 850 hPa relative vorticity field is excluded as no statistically significant correlations are found. While the correlations between the other five large-scale fields, MPI, GPI, and DAOD tend to weaken from JJA to ASO, the correlations remain significant for all of these large-scale fields, and the integrated TC indices, in the Caribbean during ASO. For the tropical North Atlantic, JJA DAOD has much weaker and insignificant correlations with JJA 200 hPa zonal wind, wind shear, and 700 hPa RH compared to those for the Caribbean. However its negative correlation with SST is as strong as that for the Caribbean in JJA and remains statistically significant from JJA to ASO, although the magnitude of the correlation is weaker in ASO. These contribute to a negative correlation with MPI and GPI and a stronger correlation during JJA than during ASO.
Part of the reason for the rapid decrease in the strength of the
correlations in the tropical North Atlantic is due to relatively low
correlations between JJA and ASO values of large-scale parameters in that
portion of the basin, indicating a lack of persistence in atmosphere/ocean
conditions when compared with the Caribbean (Table 4). The persistence of
the 700 hPa RH and 850 hPa
Correlation matrix between JJA and ASO values of 850 hPa
We next explore the relationship between JJA-averaged DAOD and two large-scale climate modes that have been documented in many studies to impact Atlantic TC activity: ENSO (e.g., Gray, 1984; Goldenberg and Shapiro, 1996; Klotzbach, 2011; Klotzbach et al., 2018) and the AMM (e.g., Kossin and Vimont, 2007; Patricola et al., 2014). El Niño typically reduces Atlantic TC activity through several mechanisms including increasing westerly wind shear especially over the Caribbean (Gray, 1984) and through upper-level tropospheric warming, causing increased static stability and inhibiting deep convection (Tang and Neelin, 2004). The AMM has also been suggested in prior research to significantly impact Atlantic TC activity (Kossin and Vimont, 2007), especially when combined with ENSO (Patricola et al., 2014). A positive phase of the AMM is associated with a warmer-than-normal tropical Atlantic, anomalously low sea level pressure and anomalously weak trade winds – all of which favor Atlantic TC formation (Kossin and Vimont, 2007).
Table 5 displays the correlations between JJA regionally averaged DAODs from the different aerosol reanalysis products and the JJA and ASO ENSO (as represented by the ONI) and AMM indices. There is a positive correlation between JJA Caribbean DAOD and the concurrent (significant at the 90 % level) and ASO ONI (significant at the 95 % level) using MRC and NAAPS-RA, and the correlations with the ONI increase from JJA to ASO. The correlation between (i) JJA tropical North Atlantic DAOD and (ii) JJA and ASO ENSO is not significant, but it is consistent with previous studies (Lau and Kim, 2007; DeFlorio et al., 2016). ENSO events climatologically intensify from boreal summer to boreal autumn (Harrison and Larkin, 1998), which may be part of the reason for the increase in significance of the correlations from JJA to ASO. In addition, this likely also explains part of the reason why Atlantic TC activity correlates more strongly with Caribbean DAOD than with tropical Atlantic DAOD, given the pronounced impact that ENSO has on the Caribbean large-scale environment (Gray, 1984). Figure S3, in which JJA composites of MRC DAOD, 850 hPa horizontal wind, and 700 hPa RH for the three top El Niño and La Niña ENSO years (based on JJA ONI) are shown, corroborates that stronger dust transport into the Caribbean occurs during El Niño years without necessarily strong emissions over Africa and high DAOD over the tropical North Atlantic. We also note that 2015 is both an El Niño and a high-DAOD year, while 2011 is both a La Niña and low-DAOD year. Removing the 2 overlapping years in the composites leads to similar results except that DAOD differences between El Niño and La Niña years are more negative in the tropical North Atlantic, additionally supporting the insignificant correlation between ENSO and tropical North Atlantic DAOD.
The correlations between JJA Caribbean DAOD and JJA AMM are consistently
strong and negative (
Correlations of MRC JJA Caribbean DAOD or JJA tropical North Atlantic (TATL) DAOD with the JJA or ASO Oceanic Niño Index (ONI) and the AMM. Correlations that are statistically significant at the 90 % level are highlighted in bold. Correlations that are statistically significant at the 95 % level are marked with an asterisks.
So far we have shown that Caribbean DAOD is correlated with Atlantic basin-wide ACE as well as two large-scale climate modes: ENSO and the AMM. Both of these modes have been shown to also impact Atlantic TC activity. To remove the influence of these climate indices from the relationship between DAOD and ACE, we use partial correlation analysis (Kleinbaum et al., 2013). Table 6 shows the partial correlation matrix between JJA Caribbean and tropical North Atlantic DAOD and annual Atlantic basin-wide ACE while controlling for the ONI and AMM indices, respectively. Removing the influence of ENSO causes little change in the negative correlation between MRC Caribbean JJA DAOD and ACE. The correlation remains statistically significant, suggesting that ENSO is not primarily responsible for the negative correlation between Caribbean DAOD and Atlantic ACE, at least during the study period. The correlation between tropical North Atlantic JJA DAOD and ACE also changes little, although the correlation is weak and not statistically significant initially. We note that the correlation between ONI and ACE is very weak and is not significant during the 2003–2018 study period, partially due to the extremely active 2004 Atlantic hurricane season which occurred despite a weak El Niño event.
Partial correlation matrix between MRC JJA Caribbean and tropical North Atlantic (TATL) DAOD and annual Atlantic basin-wide ACE while controlling for ENSO (using the JJA ONI) and the AMM (using JJA AMM index). Linear correlations (without controlling for the climate modes) between DAOD and ACE, AMM and ENSO indices, and ACE are also listed for comparison purposes. Correlations that are statistically significant at the 90 % level are highlighted in bold, and those with asterisks are statistically significant at the 95 % level. Note that the thresholds for statistical significance are different for partial correlation and linear correlation, as the degrees of freedom are different between the two.
In contrast to the findings of removing ENSO from the DAOD–ACE relationship,
after removing the influence of the AMM, the correlation between Caribbean
JJA DAOD and Atlantic ACE is much weaker and drops to insignificant levels,
suggesting that the AMM is an important factor in the dust–TC relationship.
However, it is hard to argue that the AMM is the determining factor in the
dust–TC relationship, as the correlation of ACE with JJA Caribbean DAOD is
slightly higher than with the JJA AMM (
The sensitivity of our results to the domain definitions of the tropical
North Atlantic and the Caribbean regions is explored by defining equal areas
(shifting the separation longitude to 52.5
While airborne dust can impact TC activity, once TCs form and develop, both
precipitation and strong winds can significantly remove these dust
particles. However, the removal effect by TCs cannot primarily explain the
negative DAOD–TC relationship. This is because peak dust activity occurs
from June to August, with larger DAOD in June and July than in August over
the Atlantic, suggesting that peak DAOD in general leads the peak TC season
by
The sensitivity of the composite analysis of high-JJA-Caribbean-DAOD years versus low-JJA-Caribbean-DAOD years to the number of years is also explored by using 2 and 4 years for composites in addition to 3 years (Table S4). Consistent results are found across all sensitivity tests. Atlantic basin-wide numbers of tropical depressions, named storms, hurricanes, and major hurricanes, and ACE are higher (by a factor of 1.6–5) in the low-JJA-Caribbean-DAOD seasons than in the high-JJA-Caribbean-DAOD seasons. The ratios of observed average seasonal Atlantic hurricanes and major hurricanes in the low-JJA-Caribbean-DAOD seasons versus the high-JJA-Caribbean-DAOD seasons are generally higher than those for tropical depressions and named storms, suggesting a stronger correlation relationship between dust aerosols and intense storms than weak storms.
The relationship between African dust and Atlantic tropical cyclone (TC)
activity has been analyzed in many prior studies (e.g., Dunion and Velden,
2004; Evan et al., 2006a; Braun et al., 2013; Pan et al., 2018). This study has
revisited this relationship with a statistical analysis using three newly
available aerosol reanalyses: the Naval Aerosol Analysis and Prediction
System reanalysis (NAAPS-RA), the Modern-Era Retrospective analysis for
Research and Applications, Version 2 (MERRA-2) aerosol reanalysis, the
Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), and a
multi-reanalysis consensus (MRC) based on the three reanalyses for the
period 2003–2018. The datasets are validated with ground-based observations
for modal (fine, coarse and total) aerosol optical depth (AOD). The MRC data
are primarily used in this study as it generally has better verification
results than any of the individual reanalysis products. To our knowledge,
this is the first climate study using a multi-reanalysis consensus to
represent aerosol conditions. Our findings are summarized below.
Total AODs of the three aerosol reanalysis products are similar for the
Atlantic Main Development Region; however, AOD attributed to individual
aerosol species (such as dust aerosols) can be quite different among the
three reanalysis products (Fig. 4). June–July–August (JJA) dust AOD (DAOD)
magnitude can differ by as much as 0.06, corresponding to approximately
60 % of the climatological JJA DAOD based on MRC for the Caribbean, and
can differ by as much as 0.05, approximately 20 % of the climatological
JJA DAOD based on MRC for the tropical North Atlantic. This is because total
AOD is the only aerosol property constrained by satellite observations
through AOD data assimilation in all three aerosol reanalysis products,
while speciated AOD is not constrained. This also supports the potential
usefulness of MRC, as multi-model consensus values are found to generally be better
performers than individual models in aerosol simulations (Sessions et al.,
2015; Xian et al., 2019). Despite differences in DAOD magnitude, DAODs of
the three reanalysis products correlate significantly over the tropical
Atlantic and Caribbean. Each of the three individual reanalyses and the MRC have significant and
negative correlations between JJA Caribbean DAOD and seasonal Atlantic
accumulated cyclone energy (ACE) (Table 2). High JJA DAOD in the Caribbean
is associated with a less conducive environment for hurricane activity as
represented by cooler SST, enhanced vertical wind shear, lower mid-level
moisture, and by lower maximum potential intensity (MPI) and genesis
potential index (GPI) values (Table 3). Pronounced differences in Atlantic
TC activity are seen when examining the three seasons with the highest
levels of JJA Caribbean DAOD compared with the three seasons with the lowest
JJA Caribbean DAOD (Table 4 and Fig. 7). About three times as many major
hurricanes occurred during the three lowest-DAOD seasons (2005, 2011, 2017)
compared with the three highest-DAOD seasons (2018, 2015, 2014). Atlantic TC
activity is also negatively correlated with tropical North Atlantic DAOD but
not as significantly as with Caribbean DAOD for possible reasons discussed
in the conclusion that follows. High Caribbean DAOD is typically associated with El Niño conditions;
however ENSO does not appear to significantly impact the Caribbean DAOD–ACE
relationship. The robust DAOD–ACE correlation still holds after removing
ENSO's influence via partial correlation analysis. JJA North Atlantic DAOD and the Atlantic Meridional Mode (AMM) are
intertwined in the dust–TC relationship. Both the Caribbean and tropical
North Atlantic DAODs have strong negative correlations with JJA values of
the AMM index (with a stronger correlation for the Caribbean DAOD).
Meanwhile, the JJA AMM index correlates significantly with Atlantic ACE. For
AMM and DAOD, removing the other in their relationships with ACE
dramatically reduces the significance of the correlations based on partial
correlation analysis. This result supports Evan et al.'s (2011) work, which
showed that African dust excited AMM variability on interannual to decadal
timescales through radiative forcing of the underlying SST. Consequently,
it can be argued that the negative correlation between Caribbean and
tropical North Atlantic DAOD and Atlantic TC activity may be a result of
forcing of the AMM by African dust.
These results agree with previous studies that showed negative correlations between boreal summer Atlantic dustiness and TC activity (e.g., Dunion and Velden, 2004; Evan et al., 2006a; Lau and Kim 2007). The correlations obtained in this study, especially those with Caribbean DAOD, are slightly higher than previous studies, including correlations between boreal summer dustiness and ACE (Evan et al., 2006a; Lau and Kim 2007) and between JJA dustiness and ENSO (DeFlorio et al., 2016). We note that the study areas, time periods, and study methods are not identical between our study and these previous studies, implying the usefulness of aerosol reanalyses in climate studies. Various sensitivity tests show that our results are not sensitive to the definitions of areas for the Caribbean and tropical North Atlantic, the number of composite years used, or the definition of the dust season (June–July vs. June–August).
Our results also document statistically significant relationships between Atlantic dustiness and large-scale fields (e.g., SST, vertical wind shear, mid-level moisture, and relative vorticity) and integrated TC diagnostic indices (e.g., MPI and GPI), which can be seen as confounding factors in the dust–TC relationship. Exploring the causality of the documented negative correlation between DAOD and Atlantic TC activity through modeling experiments is beyond the scope of this paper. However in some modeling studies, radiative forcing of the scattering dust alone could result in an inverse relationship between African dust and Atlantic TC activity (Strong et al., 2018; Sobel et al., 2019), along with consistent large-scale fields associated with TC activity. So it is possible that radiative forcing of the scattering dust is a dominant factor in the inverse dust–TC relationship on seasonal to interannual and basin-wide scales. After all, aerosols are coupled radiatively with meteorology in the ERA-Interim dataset, which provides the large-scale atmospheric data used in this study.
The correlations with Atlantic ACE are higher for Caribbean DAOD than for
tropical North Atlantic DAOD – a result that was not documented previously.
The differences in the relationships of Atlantic ACE with regional DAOD are
potentially due to several factors.
The large-scale environmental fields investigated herein are better
self-correlated between the JJA and ASO seasons over the Caribbean than over
the tropical North Atlantic (Table 4). Therefore, the higher correlation
with JJA Caribbean DAOD, which reflects the large-scale circulation,
especially for the low- and mid-level wind flow that modulates how far
African dust can be transported, tends to extend into the ASO peak TC
season. As was shown in Saunders et al. (2017), the strength of the
low-level winds in the Caribbean tends to be the most robust diagnostic for
seasonal ACE during the peak months of the Atlantic hurricane season. ENSO has a large impact on the Caribbean large-scale environment (Gray,
1984). ENSO-forced SST anomalies typically exhibit very strong persistence
from JJA to ASO (Harrison and Larkin, 1998). As Caribbean JJA DAOD is
correlated with ENSO to some extent, it is also correlated with Atlantic
ACE. There might be a regime shift in the integrated outcome of dust–TC
interactions from east to west across the tropical North Atlantic at climate
timescales, with the eastern tropical North Atlantic (e.g., close to the
African continent) having a positive correlation (e.g., since dust emission
is often associated with AEWs emerging from Africa; Jones et al., 2003;
Karyampudi and Carlson, 1988), while the correlation becomes more negative
heading west across the basin. The relatively larger JJA DAOD variability in the Caribbean (larger
relative dynamical range of Caribbean JJA DAOD compared to the tropical
north Atlantic JJA DAOD over 2003–2018) could contribute to a higher
correlation (Figs. 3 and 4). AOD data quality is comparatively better over the Caribbean than over the
tropical North Atlantic (Table 1; Fig. 3), given that AOD reanalyses
generally perform better over long-range transport regions than they do
closer to the aerosol source regions (Lynch et al., 2016).
The conclusions drawn from this study are based on 16-year records (2003–2018) of DAOD data from the MRC. We acknowledge that this analysis represents a relatively short time span for a climate correlation study. Longer-period aerosol reanalyses with good fidelity are needed for further climate studies. These reanalyses face several challenges including dealing with changes to the network of AOD-observing satellites over time, as well as reasonable simulations and partitioning of aerosol-speciated AODs associated with rare severe aerosol events (e.g., volcanic eruptions) within AOD data assimilation systems.
It is also worth noting that the 2003–2018 DAOD climatology for the Atlantic MDR could be low relative to the extremely dusty period of the 1980s that has been documented by long-term in situ dust concentration measurements made in Barbados (Prospero, 2014) and a 24-year eastern North Atlantic dust cover record derived from the Advanced Very High Resolution Radiometer (Evan et al., 2006a). Dust concentration records at Barbados (1965–2011) and dust cover determined from AVHRR (1982–2005) both indicate that dust levels over the North Atlantic peaked during the mid-1980s, when tropical Atlantic TC activity was relatively low (e.g., Wang et al., 2012). Our findings are consistent with the negative correlations reflected in both the earlier dust and the earlier tropical Atlantic TC activity records.
Since we note that June–July Caribbean DAOD are strongly correlated with large-scale atmospheric fields which are frequently used in seasonal hurricane forecasts, DAOD may be a useful confirmation tool for observed thermodynamic conditions. Most groups issuing seasonal hurricane forecasts provide an update in early August (immediately prior to the peak of the Atlantic hurricane season). So early season (June–July) DAOD, especially over the Caribbean, could be a potential indicator for the strength of seasonal Atlantic TC activity. While there is very strong agreement between large-scale zonal wind fields (e.g., the June–July-averaged 850 hPa zonal wind over the tropical Atlantic correlates at 0.92 between ERA-Interim and MERRA-2, in which the impact of aerosols on radiation and thereby on meteorology is included), there is less agreement with the mid-level moisture field. The correlation between ERA-Interim and MERRA-2 700 hPa RH over the tropical Atlantic averaged over June–July is only 0.72. The DAOD could potentially be used to help clarify the favorability/unfavorability of the thermodynamic environment, as indicated also by the strong correlation between JJA Caribbean DAOD and JJA and ASO MPI.
We believe this study, which shows a robust correlation relationship for the integrated dust–atmosphere–ocean system, could provide a framework to better understand the linkages between DAOD and Atlantic TC activity and how DAOD affects the large-scale environment of the MDR. We find that DAOD in both the tropical Atlantic and Caribbean is negatively correlated with Atlantic hurricane frequency, intensity, and integrated indices such as ACE, with stronger negative correlations in the Caribbean than farther east in the tropical North Atlantic. However, this study focuses on seasonal to interannual timescales and provides analysis from a large-scale point of view. Finer temporal scales (from hours to days) are needed in future studies for cases where African dust is entrained into TC vortices.
All data supporting the conclusions of this manuscript are available through the links provided below.
AERONET Version 3 Level 2 data:
The supplement related to this article is available online at:
PJK, JPD, and PX conceived the idea. PX and PJK performed most of the analysis and writing. MAJ calculated and performed the analyses on MPI and GPI. All authors contributed to the writing and revision of the manuscript.
The authors declare that they have no conflict of interest.
Philip J. Klotzbach would like to acknowledge a grant from the G. Unger Vetlesen Foundation. Peter R. Colarco is supported by the NASA Modeling, Analysis, and Prediction Program (program manager: David Considine).
This research has been supported by the Office of Naval Research (grant no. 75-8478-B-8-5) and the G. Unger Vetlesen Foundation.
This paper was edited by Jayanarayanan Kuttippurath and reviewed by two anonymous referees.