Interannual variability in Saharan dust over the North Atlantic Ocean and its relation to meteorological fields during northern winter

Atmospheric Research 122 (2013) 336–346

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Interannual variability in Saharan dust over the North Atlantic Ocean and its relation to meteorological fields during northern winter K. Nakamae a,⁎, M. Shiotani b a b

National Institute for Environmental Studies (NIES), Japan Research Institute for Sustainable Humanosphere (RISH), Kyoto University, Japan

a r t i c l e

i n f o

Article history: Received 3 November 2010 Received in revised form 11 September 2012 Accepted 11 September 2012 Keywords: Saharan dust Interannual variability North Atlantic Oscillation

a b s t r a c t In this study, we focus on interannual variability in Saharan dust transport over the North Atlantic Ocean during northern winter using the Aerosol Index data from the Total Ozone Mapping Spectrometer (TOMS). Since the TOMS observations provide a long-term global record, they are useful for considering interannual variations of the Saharan dust in relation to the North Atlantic Oscillation (NAO). We calculated correlation coefficients between the NAO Index and the TOMS AI averaged over the northern winter season. Using the correlation map, we defined three regions: Region 1 is in positive correlation over the North Atlantic Ocean, Region 2 is in insignificant correlation over the west Sahara, and Region 3 is in negative correlation over the east Sahara. Focusing on Region 1, we considered the relationship between the regional mean TOMS AI time series and certain meteorological parameters. We used sea level pressure and wind speed fields to represent the synoptic circulation, and virtual temperature and lifting condensation level (LCL) to represent the mixed layer condition. When the TOMS AI in Region 1 is large, the easterly winds over the west Sahara are stronger than usual compared to the NAO, and the virtual temperature is higher than average over the ocean. As a result, the LCL is higher near 20°W–0° and is lower than average over the desert. When dust outflow events are observed over the North Atlantic Ocean, strong winds with a high pressure blow in the west Sahara, and dust containing warm air parcels is carried by these strong winds to ocean through areas near the coast (20°N–30°N, 15°W–10°W) from the west Sahara. We summarized the features of meteorological fields during dust outflow events. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Atmospheric aerosols are one of the most important contributors to global climate change, and many efforts have been made to estimate the effect of each type of aerosol on radiative forcing. However, such efforts are not necessarily successful, because we need detailed information on the global distribution and properties of major aerosol species (Haywood and Boucher, 2000). Recently, remote sensing techniques have emerged as effective methods to identify major aerosol components, to characterize their distribution and properties, and to estimate ⁎ Corresponding author. E-mail address: [email protected] (K. Nakamae). 0169-8095/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.atmosres.2012.09.012

their local radiative forcing. Two principal methods are available to detect aerosol distributions and properties using remote sensing techniques: ground-based observations on land or on a ship, and satellite-based observations from space. Ground-based measurements such as the AErosol Robotic NETwork (AERONET) (Holben et al., 1998) and the European Aerosol Research Lidar Network (EARLINET) (Bósenberg et al., 2003; Pisani et al., 2011) have been used to monitor the vertical structure and optical thickness at local sites. Owing to recent developments in remote sensing from space, satellite-based measurements have provided new aerosol data sets in which we observe many aerosol sources worldwide. Some satellite sensors have aerosol detection capabilities, for example, Advanced Very High Resolution Radiometer (AVHRR), Total Ozone Mapping Spectrometer (TOMS), Sea-viewing Wide

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Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) (Gkikas et al., 2009), Ozone Monitoring Instrument (OMI) and, Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In particular, MODIS and SeaWiFS can monitor many dust events and occurrences of forest fire smoke every day (King et al., 1999; Antoine and Nobileau, 2006), and CALIPSO can obtain the vertical profile of the aerosol layer (Ben-Ami et al., 2009). Data sets from these satellite instruments show that the main aerosol sources worldwide are smoke aerosol from biomass burning in South America (the Amazon basin), Middle Africa, and South Africa, and mineral aerosol from the Sahara desert in North Africa, the Gobi desert, the Takla-Makan desert, and several deserts in Saudi Arabia (Prospero et al., 2002). Using these measurements many modeling studies have simulated the properties and spatial distributions of each type of aerosol (Tegen, 2003; Ginoux et al., 2001; Shao et al., 2010; Escudero et al., 2011). These studies using ground-based and satellite-based measurements and model data could describe the impact of each type of aerosol on climate and meteorology. In particular, the effect in Sahara desert, on climate is very large because it is the largest aerosol source region. Previous studies using recent satellite data showed that the African dust load was maximum in northern summer (Herman et al., 1997; Cakmur et al., 2001), and large amounts were transported across the Atlantic Ocean to the Caribbean islands and also across the Mediterranean Sea to Europe (Doherty et al., 2008; Kaskaoutis et al., 2010). Saharan dust transported to near North America across the Atlantic Ocean has been observed by ground-based instruments at Barbados (Prospero et al., 2002; Prospero and Nees, 1986; Prospero and Lamb, 2003). This Barbados data set showed clear variability in Saharan dust transport (Prospero, 1999; Prospero and Lamb, 2003), and the model simulation data indicated that the variations of dust at Barbados were linked to seasonal and interannual variability in Saharan dust emissions and the dust source region (Mahowald et al., 2002). The typical routes for dust transport across the Atlantic Ocean from source regions were described using models (Ellis and Merrill, 1995; Kallos et al., 2006). Moreover, the Saharan dust transported across the Mediterranean to Europe was measured by ground-based lidar systems in Europe (e.g., Italy, Greece, and French) (Balis et al., 2006; Gerasopoulos et al., 2011; Nastos, 2012; Kaskaoutis et al., 2010). These observational data sets in Europe also showed that dust transport through the Mediterranean varies seasonally and interannually (Israelevich et al., 2002, 2012; Papayannis et al., 2005; Koukouli et al., 2006; Ciardinia et al., 2012). Many previous studies have discussed the seasonal and interannual variation in Saharan dust emission and transport in North Africa. Some have suggested that the variation in dust source areas and dust plumes from the Sahara desert was linked with that of the Intertropical Convergence Zone (ITCZ) and the African Easterly Jet (Jones et al., 2003, 2004; Stuut et al., 2005; Sunnua et al., 2008). Several other studies have referred to the effect of atmospheric teleconnection patterns due to El Niño/ Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) on Saharan dust variability using observational data sets such as those from ground-based (Gelado-Caballero et al., 2012) and satellite-based measurements. (Prospero and Nees, 1986) suggested that the ENSO teleconnection pattern effects the

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variability of Sahel rainfall and dust transport across the tropical North Atlantic. Using the AVHRR Aerosol Optical Thickness (AOT) data sets (Evan et al., 2006), compared variations in the Saharan dust with atmospheric patterns such as ENSO and the NAO. Several studies have focused on the relationship between variability in the dust load and in the NAO during northern winter on the basis of several datasets, for example, the TOMS Aerosol Index (AI) and Meteosat Dust optical thickness (DOT) (Moulin et al., 1997; Moulin and Chiapello, 2004; Chiapello et al., 1999; Chiapello and Moulin, 2002; Chiapello et al., 2005). In a somewhat different way (Riemer et al., 2006), showed using the TOMS aerosol data for 23 years [1979–1993 and 1997–2004] that the location of the Azores High, particularly its latitude, rather than the NAO is more appropriate to explain the variability of dust transport from Africa over the subtropical Atlantic. This phenomenon was also studied using aerosol data sets calculated from the GOCART (Goddard Chemistry Aerosol Radiation and Transport) model (Ginoux et al., 2004). These previous studies pointed out the importance of horizontal winds driven by the NAO (or the Azores high position and strength), but they did not refer to the relationship with synoptic scale meteorological fields and boundary layer conditions over the Sahara desert. In this study, we focus on the relationship between Saharan dust transported over the ocean and the corresponding climatological and meteorological fields, particularly near the boundary layer, around North Africa and the North Atlantic Ocean during northern winter. We use the TOMS AI to detect Saharan dust and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/ NCAR) reanalysis data sets to describe the atmospheric conditions. We describe the aerosol and meteorological data sets in Section 2. In Section 3, we present the distributions of the TOMS AI and meteorological fields around North Africa in northern winter (December–February). The results in Section 4 are subdivided into three subsections presenting the relationship of AI with wind speed, virtual temperature, and lifting condensation level (LCL), respectively. Section 5 provides a summary and discussion of this study. 2. Data 2.1. Aerosol data Recently, global aerosol data sets have been derived from several high performance instruments such as MODIS and CALIPSO. Using these data sets, we can characterize the size distribution of each kind of aerosol and the height of the aerosol layer. However, the operational period of these instruments is not long sufficiently for analysis of interannual variability, TOMS and AVHRR provide the longest operational period. The AVHRR aerosol optical thickness (AOT) data are derived on the basis of radiances at visible and near-infrared wavelengths and are available only over the ocean (Kaufman and Holben, 1993; Higurashi and Nakajima, 1999). On the other hand, TOMS measured backscattered ultraviolet (UV) radiances at six wavelengths, and the data over both land and

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ocean are available (Torres et al., 1998). Though we cannot use them to estimate the type of aerosol, aerosol size distribution, and height of the aerosol layer, the TOMS has provided one of the longest available records of aerosol observations among satellite measurements. The Aerosol Optical Depth (AOD) retrievals need to assume surface reflectance and aerosol models, while the AI directly takes the spectral contrast. We are interested in the distribution of continental aerosols and the location of aerosol sources over land, and we need long-term observations to analyze the interannual variability. Hence, in the following study we use Aerosol Index (AI) data inferred from the TOMS to detect Saharan dust. This aerosol information is estimated from the ratio of backscattered radiances at two UV channels (for Nimbus-7 TOMS, 340 nm and 380 nm, and for Earth-Probe TOMS, 331 nm and 360 nm) over both land and ocean. The TOMS instrument can detect UV absorbing aerosols, for example, carbon aerosol due to biomass burning and mineral aerosol from the desert. However, we must note that TOMS cannot always detect aerosol layers, especially when they are located lower than about 1–1.5 km in height (Mahowald and Dufresne, 2004). The aerosol index is defined as       I I ; − log10 λ1 r λ1 ¼ −100 log10 λ1 Iλ2 meas Iλ2 calc where Iλ is the backscattered radiance at wavelength λ, ðIλ1 =Iλ2 Þmeas is the ratio of backscattered radiances measured by TOMS, and ðIλ1 =I λ2 Þcalc is the ratio of calculated radiances using a radiative transfer model for a pure Rayleigh atmosphere (Herman et al., 1997; Torres et al., 1998, 2002). In this study, we used level 3 of version 8 data products with a horizontal grid of 1.25° longitude and 1° latitude for the period of November 1978 to May 1993 from the Nimbus-7/TOMS (Bhartia et al., 2004). Another data source from the Earth-Probe TOMS is available for later dates (July 1996 to 2005 December), but we concluded that the period we selected is suitable for the analysis of long-term variability because of its stability and continuity. More detailed discussion will be described in the next section. 2.2. Meteorological data and NAO index We used the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis Data (Kalnay et al., 1996) to analyze of meteorological fields such as sea level pressure (SLP), horizontal wind (wind speed, u and v), temperature, relative humidity, and geopotential height. This reanalysis data set is produced for the period starting from 1949, and has a horizontal resolution of 2.5°× 2.5° (longitude and latitude) and 17 pressure levels. In this study we used the monthly mean data. The NAO is one of the most dominant teleconnection patterns of atmospheric variability in the North Atlantic region. It exerts a strong influence on climate and weather around the North Atlantic Ocean, Europe, the Mediterranean, North Africa, and North America, particularly during northern winter (Hurrell, 1995; Jones et al., 1997; Hurrell et al., 2003). According to George and Saunders (2001), the variability of the NAO affects that of the tropical North Atlantic trade winds, which are easterlies from North Africa to the Atlantic Ocean. In this study, we used the NAO index defined by Hurrell (1995),

who used the difference in normalized SLP between the Azores High (Lisbon, Portugal) and the Iceland Low (Stykkisholmur, Iceland). Hurrell's NAO index data set is available since 1865 on a monthly mean basis. 3. Interannual variability of Saharan dust during northern winter First, we discuss the interannual variability of Saharan dust during northern winter using the TOMS AI data from 1978 to 1993. Previous studies showed that the most prominent relationship between the variability of the NAO and that of Saharan dust on the Atlantic Ocean could be found in winter (e.g. Chiapello et al., 2005). Fig. 1 shows the correlation coefficients calculated between the NAO index and the TOMS AI averaged over northern winter for December, January, and February (DJF), when the NAO activity is most brisk and is expected to most strongly affect on Saharan dust outbreak events. In the following analyses, “northern winter” refers to DJF. Also, the year number is for January; for example, “1990 winter” means the period from December 1989 to February 1990, unless otherwise specified. Fig. 1 shows significant positive correlation coefficients over the ocean near 20°N and 25°W and negative ones over the Sahara desert near 25°N and 20°E. Although the west Sahara desert near 20°N and 5°W may be expected to be as dust source region, the correlation coefficients in this area are not very significant. Previous studies noted a positive correlation in the North Atlantic Ocean, but its causes were not clearly proposed. In this study we focus on this positive correlation in the North Atlantic Ocean, and we aim to describe atmospheric conditions around North Africa, when we observe Saharan dust outbreaks and outflow to the North Atlantic Ocean. We selected three regions on the basis of the distribution of correlation coefficients in Fig. 1: Region 1 (15°N–30°N, 30°W– 15°W), Region 2 (15°N–30°N, 15°W–15°E), and Region 3 (20°N– 35°N, 5°E–30°E). Time series for each of the regional mean values of the AI and the NAO index averaged over the northern winter season are plotted in Fig. 2. The correlation coefficients between each of the regional mean AI values and the NAO index are 0.61 for Region 1, 0.10 for Region 2 and − 0.71 for Region 3, as expected from Fig. 1. The distribution of the correlation coefficients between the NAO Index and TOMS AI shown in Fig. 1 is basically in good accordance with the previous studies (Chiapello and Moulin, 2002; Chiapello et al., 2005). However, Riemer et al. (2006) showed that the correlation coefficient decreases with increasing the analysis period. When it is from 1979 to 2005, the correlation coefficients between the NAO Index and TOMS AI are 0.27 for Region 1, 0.04 for Region 2 and − 0.25 for Region 3. The cause of decreasing correlation coefficients might be due to discontinuity of the TOMS AI data set (between the Nimbus-7 and the Earth-Probe) and due to poor condition of the Earth-Probe/TOMS instruments in the later period (after 2000) (Kiss et al., 2007). This is one of the reasons why we limit the data period from 1978 to 1993. Fig. 2 shows that the years with large TOMS AI values in Region 1 correspond to those with a large positive NAO index, for example 1989 winter, and that years with small TOMS AI values in Region 1 correspond to those with a negative NAO

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(3) (1)

(2)

Fig. 1. Correlation coefficients between the TOMS AI and the NAO index averaged over winter (DJF). Three regions are defined: Region 1, 15 N–30 N; 30 W–15 W. Region 2, 15 N–30 N; 15 W–5E. Region 3, 20 N–35 N; 5E–30E.

index, for example, 1986 winter. These two typical winters will be further described in Fig. 3, and in the next section we will make composite analyses on the basis of the years with large and small TOMS AI values in Region 1. The averaged values of the TOMS AI over the observation period from 1979 to 1993 in Regions 2 and 3 are 1.03 and 0.68, respectively; these are higher than that in Region 1 (0.52). This suggests that Regions 2 and 3 are located near the dust source area in the Sahara desert, and hence the Saharan dust over

Region 1 might be transported from Region 2 or Region 3. In addition, the variances are 0.09 for Region 1, 0.32 for Region 2, and 0.16 for Region 3. In Region 2, the average and variance of the TOMS AI time series are largest, indicating that many dust events occur in this area. Moulin et al. (1997) mentioned possibility that precipitation is one of the processes to induce interannual variability in the transport of dust and the emission over the source regions. Using the GOCART model, Ginoux et al. (2004) showed a positive correlation with the NAO index over

Fig. 2. Time series of the regional mean values in each of three regions and the NAO index averaged over DJF for the period from 1979 winter to 1993 winter. Solid line represents the time series of the NAO index; dashed, dotted, and dot-dashed lines represent the TOMS AI in Regions 1, 2, and 3, respectively. Left vertical axis shows the TOMS AI, and right vertical axis shows the NAO index.

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(a)

(b)

Fig. 3. Distribution of the TOMS AI, represented by gray scale and contours with an interval of 0.2. Synoptic circulation is also given for sea level pressure (contours) with a contour interval of 2 hPa, and surface wind vectors (reference vector is 4.0 m/s) averaged over DJF in the North Africa region for (a) 1989 winter and (b) 1986 winter.

the source region of Saharan dust somewhere close to Regions 2 and 3 in our study. Several previous studies indicate that the Saharan dust sources are located in Regions 2 and 3, for example, Mauritania, Mali, southern Algeria, and Libya etc. (Doherty et al., 2008; Knippertz et al., 2011; Moxim et al., 2011; Washington et al., 2003). However, it is difficult for us to identify these dust source regions by only the TOMS AI data set and we will explain this reason below. As shown in Figs. 1 and 2, the correlation coefficient between the NAO index and the TOMS AI in Region 1 is significantly positive, and that between the NAO index and the TOMS AI in Region 3 is significantly negative. However, the correlation

coefficient between the two time series of the regional mean AI values in Regions 1 and 3 is not very significant (−0.12). This suggests that the variation in TOMS AI in Regions 1 and 3 is connected through the influence of the NAO, which will be explained in detail later. In addition, the correlation coefficient between the NAO index and the TOMS AI in Region 2, which is expected to be a dust source region, is not significant. Note that the TOMS AI data are retrieved quantitatively as column integral aerosol amounts, and consequently they are less sensitive to aerosols at a low altitude. Thus, this index may be unable to conclusively determine dust sources at the bottom of the atmosphere (Mahowald and Dufresne, 2004). Nevertheless,

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we may say that the TOMS AI values presumably show dust floating much higher than the surface. Fig. 3 displays the distributions of the TOMS AI and the synoptic circulation represented by the SLP and wind fields averaged over the northern winter for two typical cases. Fig. 3(a) is for a year with large AI values in Region 1 (1989 winter mean), and it is a typical case of dust outflow from the Sahara to the ocean. Fig. 3(b) is for a year with relatively small AI values in Region 1 (1986 winter mean). The winter-time distributions of TOMS AI and the synoptic circulation as shown in Fig. 3(a) and (b) have some common characteristics. During this season, north Africa, southern Europe, and the North Atlantic Ocean are covered with a high pressure system, and a northeasterly blows in the west Sahara along the east side of high pressure. Maximum AI values are seen around the north side of Lake Chad (15°N and 15°E) and in the subtropical region around the Sahel, near 10°N and lower latitudes. From Fig. 3, the large values around Lake Chad are believed to be Saharan dust, and from Fig. 1, the Sahara desert area (northward of 15°N) shows significant correlation with the NAO index. The large values over the subtropical region suggest that the aerosol layer located southward of 15°N is transported to the ocean. This aerosol layer contains large amounts of carbon aerosol due to biomass burning, because the Sahel is in the dry season during northern winter. (Haywood et al., 2008) showed that observed aerosol plumes in Sahel are the complicated mixture of mineral dust and black carbon aerosol from biomass burning. They indicated that aerosol from biomass burning activity during the dry season in Sahel contributed to aerosol loading in the tropical Atlantic (near 11°N), and it is difficult to separate aerosol components using the TOMS satellite data. Based on this, we restrict ourselves to latitudes northward of 15°N by focusing on the distribution of AI northward of 15°N, because the aerosols around here would be primarily dust. In addition to these common features, distinctive differences appear between Fig. 3(a) and (b). Fig. 3(a), with large AI values in Region 1, shows that the center of high pressure is located near 40°N and 5°W, and the easterly wind in the west Sahara (Region 2; 20°N–30°N) was stronger than usual. A northeast wind blows over the east Sahara (Region 3) from the Mediterranean. This case corresponds to the meteorological conditions of the NAO positive phase when the related wind field around the high pressure center is stronger than usual; that is, this north–south pressure contrast drives the mean winds. When the AI values in Region 1 are small, as shown in Fig. 3(b), the center of high pressure is located near 30°N and 30°W, and the northerly wind around the Mediterranean coast on the North Africa side (30°N, 10–30°E) toward Region 3 is weaker than in Fig. 3(a). Because the high pressure center was located in the North Atlantic Ocean, the easterly wind region shifted southward from the west Sahara, and the easterlies were weaker over the dust source. Prevailing winds over the Mediterranean were westerly and did not blow into the east Sahara. This condition corresponds to the NAO negative phase, and the north–south pressure contrast is smaller and the wind is weaker than in Fig. 3(a). We assume that dust events occur frequently in the Sahara desert, but dust outflow is observed only over the Atlantic Ocean (Region 1) under the specific meteorological conditions

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shown in Fig. 3(a). We will consider the difference between the two cases in detail in the next section. 4. TOMS AI and meteorological fields In this section, we focus on the two typical cases mentioned above on the basis of composite analysis. In one case, dust outflow over Region 1 is observed, and in the other, it is not. In the following, we use meteorological elements such as wind speed, virtual temperature, and lifting condensation level (LCL). The surface wind speed could be associated with the location of the dust source regions, and the virtual temperature and LCL can be used to represent the mixed layer conditions. 4.1. Wind speed In the previous section, we showed the surface pressure fields and surface winds in two typical years, and we described the difference in the high pressure centers between the two cases in Fig. 3. It is known that variability in the surface pressure field leads to a change in the surface wind field and that variability in the wind field plays a substantial role in dust loading. In the following, we will show the difference between composites of the meteorological fields during winter in years with larger and smaller TOMS AI values in Region 1. They were chosen from Fig. 2, which shows the time series of the NAO and the regional mean AI. We selected 1983, 1989, and 1992 as years having larger AI values and 1986, 1988, and 1991 as years having smaller AI values. In the following analysis, we use Case A to refer to a composite of years having larger AI values in Region 1 and Case B to refer to a composite of years having smaller AI values in Region 1. Fig. 4 shows the horizontal distribution of differences in the wind speed as shown shaded contour and the horizontal component of the wind as shown vectors between Case A and Case B, representing the region that shows the most prominent variation in wind speed in relation to the AI time series in Region 1. Because the differences near Region 2 are positive, the wind speed over Region 2 is stronger in Case A than that in Case B, suggesting that the AI in Region 1 would be affected by the wind speed near Region 2. This result corresponds to Fig. 3 of Riemer et al. (2006). The averaged wind directions are easterly in winter, as shown in Fig. 3, suggesting that dust above the west Sahara desert could be transported to the North Atlantic Ocean by the stronger wind. This does not contradict the model simulation data of Mahowald and Dufresne (2004). It has been already mentioned in Brooks and Legrand (2000) that the dust source in the west Sahara desert corresponds to the area in northern Mauritania, northern Mali and southwestern Algeria. The wind speed enhanced by the NAO may have increased the dust emission and, it is closely related to humidity of the atmospheric boundary layer. The relationship between the Saharan dust events and the condition of the atmospheric boundary layer are discussed in detail in Subsections 4.2 and 4.3. The differences over Region 3 (the east Sahara) have small positive values. Since the averaged wind direction is northwesterly in Fig. 3, in Case A the northwesterly winds (from the Mediterranean Sea to the east Sahara) over Region 3 are stronger than that in Case B. In other words, when the northwesterly wind from the Mediterranean Sea is stronger,

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Fig. 4. Difference in composite of the wind speed (contour interval is 0.4) and the horizontal component of wind vectors (reference vector is 2.0 m/s) between Cases A and B. Positive values with shading show areas where wind speeds in Case A are larger than those in Case B.

the TOMS AI in Region 3 is smaller. This result is relatively inconsistent with the known relationship of wind speed and dust emission. There is a related result on the aerosol source located in northeastern Sudan to southern Egypt by Brooks and Legrand (2000), showing that the dust has been transported to the eastern Mediterranean when weak cyclonic circulation is located in the Mediterranean. This contradiction will be considered in Subsections 4.2 and 4.3. 4.2. Virtual temperature The virtual temperature is defined for moist air in lower troposphere, and it is used to estimate the mixed layer height on the basis of its gradient or lapse rate because the mixed layer is usually capped by an inversion. In this subsection, we will discuss vertical thermal structures, including the boundary layer, and in the next subsection we will roughly estimate the mixed layer height, though the vertical resolution of the NCEP/NCAR reanalysis data is rather coarse for accurate estimation. A characteristic contrast in thermal structures in the lower atmosphere below 3 km exists between the desert in North Africa and the North Atlantic Ocean, because high temperatures and dry air occur over the desert and low temperature and wet air occur over the ocean. Fig. 5 shows a line graph of the virtual temperature along longitude averaged over 15°N– 30°N at 925 hPa, in which we can see large differences most easily. The dashed line represents the composite values of Case A, and the solid line represents those of Case B. The virtual temperature is slightly higher over the ocean (Region 1) and lower over the desert (Region 2) in Case A than in Case B. As a result, the differences between the virtual temperature over the ocean and that over the desert were relatively smaller in Case A. We can conjecture from these results that heated air parcels over the desert are transported by the stronger winds in Region 2, and consequently, the virtual temperature over the

ocean (Region 1) was slightly higher in Case A. At this time, the virtual temperature over Regions 2 and 3 tended to be lower because cold northeast winds blew from the Mediterranean to the Sahara desert (Region 3), as shown in Fig. 3. 4.3. Lifting condensation level In this subsection, we focus on the lifting condensation level (LCL) to estimate the mixed layer height. The LCL is usually used to estimate the height of the cloud base and can be determined by the meteorological parameters of surface temperature and relative humidity. If the surface temperature is relatively low and the humidity is high, which is typical of the marine boundary layer (for example, over Region 1), the LCL may be lower than the top of the mixed layer. If surface temperature is relatively high and the humidity is low, which is typical of the boundary layer on the desert (for example, over Regions 2 and 3), the LCL may be higher than the actual top of the mixed layer. Dust is believed to have been raised by the wind and lifted to the top of the mixed layer. The mixed layer grows with time during the daytime and reaches its maximum height at midday in local time. During the nighttime, stable boundary layer is gradually built up by advection of warmer air over a cooler surface. Because the TOMS measured backscattered UV radiation during the daytime, the LCL in this study is calculated using meteorological parameters at 12 UTC, which is in the daytime around the Sahara desert and the Sahel. We calculated that the height of the LCL over the Sahara desert is approximately 4–5 km in winter. Fig. 6 displays line plots of the LCL composites averaged over 15°N–30°N for Case A (dashed line) and Case B (solid line). The difference in virtual temperatures (contour lines with shading for positive values) and the east–west wind component (arrows) between the composites for Cases A and B are also shown. Fig. 6 shows that the mixed layer height defined by the

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Fig. 5. Line graph displaying virtual temperature along longitude averaged over 15°N–30°N at 925 hPa. Dash line represents composite values of Case A and solid line represents those of Case B.

LCL changes dramatically around the coast near 15°W. The LCL was slightly higher over the coast (near 10°W–15°W) and was lower over the desert (east of Regions 2 and 3) in Case A than in Case B. As suggested in the previous section, Fig. 6 shows heated air parcels over the ocean (Region 1; near 20°W, 1000–800 hPa) and stronger easterly winds over the west Sahara (Region 2; near 10°W–0°). The horizontal distribution of the differences in the two LCL composite values (not shown) is larger along the northwest African coast (20°N–30°N, 20°W–5°W) and smaller around the center of the Sahara desert (15°N–30°N, 5°E–30°E) in Case A than in Case B. These results suggest that in Case A, heated air parcels on the desert were transported by easterly winds from the desert, where the mixed layer is well developed, to the ocean, where the mixed layer is low compared with Case B. 5. Summary and discussion In this paper, we have investigated the association between interannual variation in Saharan dust outbreak events over the Atlantic Ocean and the meteorological fields around the North Atlantic Ocean and North Africa. We used TOMS AI data to detect Saharan dust in northern winter for 15 years, December 1978–February 1993. Using the TOMS AI and the NAO index averaged over the winter season (December, January, and February), we first calculated the correlation coefficients between the two. From this result, we divided the objective area into three regions: Region 1 (15°N–30°N, 30°W–15°W), Region 2 (15°N–30°N, 15°W–5°E), and Region 3 (20°N–35°N, 5°E–30°E). The correlation coefficients between each regional mean AI and the NAO index are 0.61 for Region 1, 0.10 for Region 2, and − 0.71 for

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Fig. 6. Line graph showing the two LCL composites averaged over 15°N– 30°N for Case A (dashed line) and Case B (solid line). Contour and arrows along pressure levels show differences between the composites of Cases A and B in virtual temperature (shading indicates positive values) and east– west wind component, respectively.

Region 3. However, Riemer et al. (2006) showed that the correlation coefficient decreases with increasing the analysis period, and that the latitudinal position of the Azores High is an important parameter that displays the higher correlation than the NAO Index with the dust over the North Atlantic Ocean. Region 1 is situated over the North Atlantic Ocean, and the Saharan dust in Region 1 (over the ocean) is believed to be transported from the dust source in the Sahara desert (Region 2 or 3). The variation in the TOMS AI in Region 2 does not show significant correlation with the NAO index, although Region 2 is situated over the desert and is expected to correspond to the Saharan dust emission area as calculated by a model simulation (Ginoux et al., 2004). It is believed that the dust source in Region 2 corresponds to the area in northern Mauritania, northern Mali and southwestern Algeria, and that in Region 3 correspond to the area in northern Sudan to southern Egypt (Brooks and Legrand, 2000). Related meteorological conditions such as SLP, horizontal wind, virtual temperature, and LCL around the Atlantic Ocean and the Sahara desert show clear differences between years with large AI values (Case A) and small AI values (Case B) over Region 1. Case A corresponds to the NAO positive phase, and Case B corresponds to the NAO negative phase (Fig. 2). Fig. 7 shows a schematic diagram of vertical [Fig. 7(a)] and horizontal [Fig. 7(b)] views for Case A. In this situation, North Africa (the Sahara desert) is covered by a high pressure field, the center of which is located near 38°N and 15°W [see Fig. 3(a)], and strong winds over Regions 2 and 3 in the desert area flow to the ocean. First, we summarize the vertical meteorological fields in Fig. 7(a). From Fig. 5, we could see that the virtual temperature is higher than average over ocean (Region 1) and lower than

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average over desert (Regions 2 and 3). This means that the air in the marine boundary layer is warmer and that in the continental boundary layer is cooler than usual; see the contours with shading in Fig. 6. Fig. 6 also showed that the LCL is higher than average near the coast (20°W–0°) and lower than average over the Sahara desert (Regions 2 and 3). This means that the mixed layer seems to develop near the coast and that its height over the Sahara desert (Regions 2 and 3) is lower than usual. In other words, this suggests that warm air parcels containing dust are carried to the ocean by easterly winds. Next, we summarize the horizontal meteorological field shown in Fig. 7(b). The variability in the TOMS AI in Region 1 was related to wind speeds in Region 2 (Fig. 4). As shown in Fig. 3(a), the wind direction is easterly from the Sahara desert to the North Atlantic Ocean on the southern side of the high pressure field (15°N–30°N), that is, in the west Sahara (Region 2), and northerly from the Mediterranean Sea to the east Sahara (Region 3). The mixed layer height defined by the LCL is higher than usual near the coast (Fig. 6), and warmer air is located over the ocean (Region 1) (Fig. 5). Therefore, it is likely that warm air parcels containing dust are carried to the ocean (Region 1) through the high LCL area near the coast (20°N–30°N,15°W–10°W) by easterly winds over the west Sahara. Finally, we briefly discuss the reason that the correlation coefficient between the TOMS AI and the NAO index in Region 3 is negative (see Fig. 2). This may be linked with the mixed layer height in Case A, which tends to be lower over

the Sahara desert (Regions 2 and 3) than in Case B, owing to wet air transported from the Mediterranean. On the other hand, the TOMS AI in the Sahara desert would tend to be lower in this case than in Case B. This could be attributed to the instrumental performance of the TOMS, which may be less sensitive to dust particles at low altitude. As seen in Figs. 3(a), 5, and 6, cold wet air transported by northerly winds from the Mediterranean to the Sahara desert lowers the mixed layer height as defined by the LCL in Case A compared to Case B. Thus, it is likely that the height of the dust layer in Case A is also lower than that in Case B. Regarding the difference between Cases A and B, it would be important to compare the location and strength of the high pressure center between the two as shown in Fig. 3, which causes the difference in wind speed over the west Sahara owing to NAO variability. In Case B, shown in Fig. 3(b), northwesterly winds over the west Sahara were weaker, and heated air parcels with dust from the desert seem to be trapped by westerly winds over the Mediterranean. This idea could be supported by LCL values over the desert in Cases A and B (see Fig. 6); that is, the LCL is larger in Case B than that in Case A. This suggests that the mixed layer height as defined by the LCL in Case B is higher than that in Case A, and dust particles are lifted with heated air parcels; hence, the TOMS AI values in Region 3 in Case B (NAO negative phase) are larger than that in Case A (NAO positive phase). As discussed above, differences in meteorological conditions clearly exist between years with and without Saharan dust outbreak events over the Atlantic Ocean. In this paper,

(a) [hPa] 600

Lifting Condensation Level

700

wind

warm

cold

850 925 North Atlantic Ocean 20W

(b)

warm air

Sahara desert 0

20E

high LCL

wind

strong wind

Fig. 7. Schematic diagrams of (a) vertical view and (b) horizontal view for Case A with large TOMS AI in Region 1.

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