Maintenance of Lower Tropospheric Temperature Inversion in the Saharan Air Layer by Dust and Dry Anomaly

Sun Wong Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

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Andrew E. Dessler Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

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Natalie M. Mahowald Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York

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Ping Yang Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

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Qian Feng Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

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Abstract

The role of Saharan dust and dry anomaly in maintaining the temperature inversion in the Saharan air layer (SAL) is investigated. The dust aerosol optical thickness (AOT) in the SAL is inferred from the measurements taken by Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the corresponding temperature and specific humidity anomalies are identified using the National Centers for Environmental Prediction (NCEP) data in August–September over the North Atlantic tropical cyclone (TC) main development region (MDR; 10°–20°N, 40°–60°W). The authors also study the SAL simulated in the National Center of Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), coupled with dust radiative effect. It is found that higher AOT is associated with warmer and dryer anomalies below 700 hPa, which increases the atmospheric stability. The calculated instantaneous radiative heating anomalies from a radiative transfer model indicate that both the dust and low humidity are essential to maintaining the temperature structure in the SAL against thermal relaxation. At 850 hPa, heating anomalies caused by both the dust and dry anomalies (for AOT > 0.8) are 0.2–0.4 K day−1. The dust heats the atmosphere below 600 hPa, while the dry anomaly cools the atmosphere below 925 hPa, resulting in a peak of heating rate anomaly located at 700–850 hPa. In the eastern Atlantic, dust contributes about 50% of the heating rate anomaly. Westward of 40°W, when the dust content becomes small (AOT < 0.6), the heating rates are more sensitive to the water vapor profile used in the radiative transfer calculation. Retrieving or simulating correct water vapor profiles is essential to the assessment of the SAL heating budgets in regions where the dust content in the SAL is small.

Corresponding author address: Sun Wong, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77840-3150. Email: swong@neo.tamu.edu

Abstract

The role of Saharan dust and dry anomaly in maintaining the temperature inversion in the Saharan air layer (SAL) is investigated. The dust aerosol optical thickness (AOT) in the SAL is inferred from the measurements taken by Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the corresponding temperature and specific humidity anomalies are identified using the National Centers for Environmental Prediction (NCEP) data in August–September over the North Atlantic tropical cyclone (TC) main development region (MDR; 10°–20°N, 40°–60°W). The authors also study the SAL simulated in the National Center of Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), coupled with dust radiative effect. It is found that higher AOT is associated with warmer and dryer anomalies below 700 hPa, which increases the atmospheric stability. The calculated instantaneous radiative heating anomalies from a radiative transfer model indicate that both the dust and low humidity are essential to maintaining the temperature structure in the SAL against thermal relaxation. At 850 hPa, heating anomalies caused by both the dust and dry anomalies (for AOT > 0.8) are 0.2–0.4 K day−1. The dust heats the atmosphere below 600 hPa, while the dry anomaly cools the atmosphere below 925 hPa, resulting in a peak of heating rate anomaly located at 700–850 hPa. In the eastern Atlantic, dust contributes about 50% of the heating rate anomaly. Westward of 40°W, when the dust content becomes small (AOT < 0.6), the heating rates are more sensitive to the water vapor profile used in the radiative transfer calculation. Retrieving or simulating correct water vapor profiles is essential to the assessment of the SAL heating budgets in regions where the dust content in the SAL is small.

Corresponding author address: Sun Wong, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77840-3150. Email: swong@neo.tamu.edu

1. Introduction

The Saharan air layer (SAL) is a well-mixed layer of dry, warm air located between the marine boundary layer and the ∼500-hPa level. From late spring to early fall, the SAL is frequently advected westward with Saharan dust across the North Atlantic Ocean (Carlson and Prospero 1972; Dunion and Velden 2004; Karyampudi et al. 1999; Nalli et al. 2005, 2006; Prospero and Carlson 1972; Wong and Dessler 2005; Wong et al. 2006). Using the Geostationary Operational Environmental Satellite (GOES) to track the SAL across the North Atlantic Ocean, Dunion and Velden (2004) found a phenomenological connection between the SAL and the suppression of the tropical cyclone (TC) activity. They suggested that the dissipation of deep convection occurring in the interior of the SAL plays a role in suppressing the development of TC activity. Using satellite-derived aerosol optical thickness (AOT) and cloud brightness temperature, Wong and Dessler (2005) verified the suppression of deep convections associated with the warm and dry anomalies of the SAL. Because of the important role of the SAL in TC activity and convection, it is necessary to improve the current knowledge about the properties and evolution of the SAL.

Wong et al. (2006, 2008) found that variations in summertime dust transport are associated with temperature anomalies near 850 hPa. These temperature variations drive the variations in the circulation at 700 hPa via the thermal wind relation, which has previously been identified to be responsible for advecting Saharan dust off the coast of West Africa (Carlson and Prospero 1972; Colarco et al. 2003; Jones et al. 2004; Karyampudi and Carlson 1988; Kaufman et al. 2005; Wong et al. 2006). Because the dust and warm anomaly in the SAL are both advected by the winds in the lower troposphere, it raises a key question of what role, if any, the dust plays in maintaining the thermal structure of the SAL.

Although many studies have focused on dust radiative forcing at the surface (srf) and the top of the atmosphere (TOA; Carlson and Benjamin 1980; Evan et al. 2008; Li et al. 2004; Myhre et al. 2003; Weaver et al. 2002; Yoshioka et al. 2007; Yu et al. 2006), little effort has been made to analyze the vertical distribution of heating caused by dust. Carlson and Benjamin (1980) calculated the dust radiative heating rates based on the observed dust distribution over the eastern Atlantic and available optical properties of dust. They noticed significant heating rates by dust (>1 K day−1) in the SAL, which suggested that the dust plays an important role in stabilizing the atmosphere. Moreover, they found that the maximum effect of dust on the heating rates is located at ∼700 hPa, where dust has the highest concentration. However, the dust optical properties used in Carlson and Benjamin (1980) are more absorptive than those found in recent observations (Colarco et al. 2002; Dubovik et al. 2002; Kaufman et al. 2001; Sinyuk et al. 2003). Using the Stratospheric Aerosol and Gas Experiment (SAGE) II retrieved vertical dust profile, Zhu et al. (2007) estimated the clear-sky net heating due to dust near the Saharan coast to be about 0.2–0.3 K day−1 below 3 km.

The intent of this study is to quantify the effects of both the dust and the dry anomaly associated with the SAL on the atmospheric heating rates, aimed at a better understanding of their implications on the thermal structure of the SAL. To do this, we first investigate the thermal and moisture structure of the SAL. Then we employ a radiative transfer model to estimate the instantaneous radiative heating anomaly caused by the dust and dry anomaly in the SAL.

2. Data and models

In this study we use dust as a tracer to track the SAL (Evan et al. 2006; Wong and Dessler 2005). The atmospheric temperature and specific humidity anomalies associated with the SAL are obtained by binning the reanalysis temperature profiles from the National Center of Environmental Prediction (NCEP)/Department of Energy Global Reanalysis 2 (NCEP-2; Kanamitusu et al. 2002) according to the Moderate Resolution Imaging Spectroradiometer (MODIS; Kaufman et al. 1997; King et al. 2003; Yu et al. 2004) AOT data. The specific humidity from the NCEP final analyses (FNL), which are forecast data and more consistent with the dropsonde data of humidity and the GOES SAL tracking imagery (Dunion and Velden, 2004), is used in the present radiative transfer calculation.

The MODIS aerosol retrieval algorithm is based on six MODIS bands (0.55–2.1 μm), and the retrieved aerosol properties are provided with a 10-km resolution (Kaufman et al. 1997; Levy et al. 2003; Remer et al. 2002). Radiances at these wavelengths are essentially due to reflected solar radiation and thus only available during daytime. The AOT at 0.55 μm (hereafter AOT) are archived in the MYD04 level-2 product (collection 5) from the Aqua satellite, which has an overpass time of approximately 1330. We averaged the level-2 AOT data into 2.5° × 2.5° boxes covering the tropical North Atlantic to match the horizontal grid of the NCEP datasets. A sensitivity test has shown that the cloud fraction of the chosen MODIS AOT pixels does not influence the results shown in this study. Hereafter, we pick those pixels with a cloud fraction less than 75%.

MODIS AOT includes the contributions from many types of aerosols. In the tropical and subtropical North Atlantic Ocean, the main contribution to AOT is from dust, maritime aerosol, and aerosol from biomass burning in South Africa. MODIS retrievals do not distinguish the fractions of different types of aerosols. Using MODIS-measured fractions of fine aerosols in the total AOT, Kaufman et al. (2005) determined the dust contribution in the total MODIS AOT over the tropical North Atlantic. Here we apply the same method to estimate the dust contribution of the MODIS-measured AOT. The dust optical thickness (τdu) is determined by
i1520-0442-22-19-5149-e1
where τ and f are the MODIS AOT and fraction of fine mode particles; fdu (=0.5), fan (=0.9), and fma (=0.3) are the empirical fractions of fine mode particles in dust, anthropogenic, and marine aerosols, respectively; and τma is proportional to the NCEP surface wind speed (=0.007W + 0.02, where W is the NCEP surface wind speed). There may be uncertainties in the determined dust AOT because of the uncertainties in the input parameters; however, because of the large amount of dust (see Table 1) involved in our calculations, these uncertainties do not generate significant differences in our results. Hereafter, total AOT and dust AOT are referred to as AOT and dust AOT, respectively.

To estimate the radiative heating by dust, vertical profiles of dust concentrations are necessary. We calculate the vertical distributions of dust in four size bins (0.1–1.0, 1.0–2.5, 2.5–5.0, and 5.0–10 μm in diameters) from the climatologically monthly mean of 1979–2000 from a simulation of the Model of Atmospheric Transport and Chemistry (MATCH; Rasch et al. 1997). The simulation is driven by NCEP reanalysis meteorological fields (T62 resolution, ∼1.8° × 1.8°, and 28 vertical levels), which have been extensively compared to observations for their climatology distribution, as well as daily to interannual variability (Luo et al. 2003; Mahowald et al. 2003, 2002; Zender et al. 2003; Hand et al. 2004). The vertical distributions of the dust AOT at a visible wavelength (0.64 μm) are used to estimate the vertical profiles of radiative heating rate by dust. We rescale the relative particle size distribution of dust to be larger so that it is consistent with observations (Reid et al. 2003; Hand et al. 2004). We also lift the dust distribution by 100 hPa, so that its peak falls in the climatological range of dust layers (∼2–4 km) observed in the Cloud–Aerosol Lidar Infrared Pathfinder Satellite Observation (CALIPSO) level-2 aerosol layer product (version 2.01; Winker et al. 2003) in August–September 2006.

Radiative heating rates are computed from a radiative transfer model (hereafter Chou’s model) that was developed at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (Chou and Suarez 2002; Chou et al. 2003). In this model, shortwave reflection and transmission associated with a dusty atmosphere are computed on the basis of the δ-Eddington approximation for 11 spectral bands (Chou and Suarez 2002). The longwave radiation is largely accounted for in a parameterization form that includes the absorption and scattering by aerosols for nine spectral bands (Chou et al. 2003). The optical properties of mineral dust (extinction coefficient, single-scattering albedo, and asymmetry factor) are specified as functions of spectral bands and particle sizes in an approach similar to those used by Mahowald et al. (2006) and Yoshioka et al. (2007). The indices of refraction have been derived from Patterson (1981) for visible wavelengths, Sokolik et al. (1993) for the near-infrared spectrum, and Volz (1973) for the infrared spectrum. The imaginary part of the visible wavelength refraction indices have been linearly interpolated from the observation-based estimates by Sinyuk et al. (2003) and Dubovik et al. (2002) at 0.33, 0.36, 0.44, 0.66, 0.87, and 1.22 μm for the spectral region of 0.33–0.97 μm. Table 2 shows the single-scattering albedos in the visible wavelength bands for the four size bins. These values are consistent with those inferred from satellite- and ground-based remote sensing data (Colarco et al. 2002; Dubovik et al. 2002; Kaufman et al. 2001).

We also analyze simulations from the Community Atmosphere Model, version 3 (CAM3; Collins et al. 2006), in which online dust transport and radiative feedbacks are implemented (Mahowald et al. 2006; Yoshioka et al. 2007). These simulations have been compared to available observations (Mahowald et al. 2006; Yoshioka et al. 2007). The dust and the dry anomaly of the SAL in the simulation are analyzed. We further perform offline heating rate calculations using these simulated dust and dry anomaly profiles. By comparing the dust, thermal, and moisture simulations with those in the MATCH and reanalysis data, we can evaluate the model’s ability for simulating the SAL. Hereafter, we refer to the calculations that use the MODIS, MATCH, and NCEP datasets as the “MODIS–MATCH” and those using CAM3 simulation as the “CAM3.”

3. Results

a. Temperature and moisture anomalies in the SAL

Figure 1 shows the MODIS and CAM3 AOT climatologies for August–September from 2003 to 2006. Both MODIS and CAM3 data indicate a large amount of aerosols transported from the Saharan desert to the tropical Atlantic. The geographical patterns of MODIS and CAM3 AOTs are similar, although CAM3 simulates higher AOTs in the western Atlantic.

We divide the TC main development region (MDR; 10°–20°N, 20°–60°W) (Goldenberg et al. 2001) into three subregions, as shown in Fig. 1. Region 1 covers the longitude range of 20°–30°W, region 2 covers 30°–40°W, and region 3 covers 40°–60°W. For each of these regions, we associate each daily 2.5° × 2.5° MODIS AOT with the collocated daily NCEP reanalysis temperature profile. To provide the temperature response as a function of AOT, the temperature profiles are then averaged for various AOT bins. The optical thickness bins used are 0–0.3, 0.3–0.4, 0.4–0.5, 0.5–0.6, 0.6–0.8, and <0.8. We define the temperature anomaly profiles to be the difference of the averaged temperature profile in each bin and the profile for AOT less than 0.3. The results are shown in Figs. 2a–c. Similar analyses are applied to CAM3’s AOT and temperature profiles, and the results are shown in Figs. 2d–f.

In the NCEP reanalysis, there are warm anomalies in all regions below ∼600 hPa, with the maximum anomaly located at 850 hPa in region 1 and rising to 700 hPa in region 3 (Figs. 2a–c). Above the warm anomalies lie the cold anomalies, with the maximum cold anomaly located at about 500 hPa. The anomalies simulated by CAM3 are qualitatively similar but with a magnitude about twice that found in NCEP reanalyses. Moreover, the cold anomalies in the midtroposphere are missing in the CAM3 simulation, and the maximum of the warm anomaly in region 1 for AOT > 0.8 is located in a lower altitude (925 hPa). The lifting of the warm anomaly maximum across the Atlantic is also not evident in the CAM3 simulation. In regions 2 and 3, CAM3 simulates fewer large AOT events, and we omit any case with a sampling size less than 10.

The SAL is also associated with dry air. To illustrate this, we apply the same technique to compute the specific humidity anomalies as we did for the temperature, and the results are shown in Fig. 3. All three regions show dry anomalies below 300 hPa, reaching the maximum around 850–925 hPa in region 1 and 700 hPa in region 3 for the NCEP data. The dry anomalies in the CAM3 are about twice that found in the NCEP data (as are the temperature anomalies in the lower troposphere in the CAM3, see Fig. 2).

The inversion layers (the maximum temperature and minimum humidity anomalies) shown in NCEP and CAM3 data are both significant at the 99% confidence level by Student’s t tests. The inversion layers rise in altitudes as they are transported from region 1 to region 3, which is consistent with the rise of the SAL base when it is transported westward (Karyampudi et al. 1999). Dunion and Marron (2008) compared the rawinsonde measurements of temperature and humidity for the non-SAL and SAL conditions in 2002 over the western tropical Atlantic and the Caribbean Sea (west of 60°W). They found a temperature increase in 500–700 hPa and maximum dry anomaly located at 500 hPa, consistent with the rising SAL base shown in the NCEP reanalysis. Dunion and Velden (2004) used GPS dropsondes to estimate the dry anomaly in the SAL in the western tropical Atlantic to be 2.5–5.5 g kg−1 between 600 and 850 hPa and extend up to 300 hPa. Radiosonde observations of the SAL (Nalli et al. 2005; Nalli et al. 2006; Szczodrak et al. 2007) also show similar dry anomaly profiles in the SAL. Our dry anomalies estimated from NCEP forecast data and the CAM3 simulation also extend up to about 300 hPa; however, our anomalies in the lower troposphere over the western tropical Atlantic are smaller in magnitude (NCEP’s 0.5–1 g kg−1 and CAM3’s 0.5–2.5 g kg−1). This is because the coarse-grid models average out spatial variation smaller than the grid size and/or there may be biases in the model simulations.

b. Radiative heating flux anomaly due to dust

To understand the role that dust plays in the temperature anomaly in the SAL, we estimate the radiative heating anomaly due to dust. Since the SAL contains both dust and dry air, we also estimate the heating anomaly caused by both the dust and the dry anomaly (hereafter, the SAL heating anomaly). For all calculations, the background atmosphere is prescribed by the averaged temperature and water vapor profiles for AOT less than 0.3 in each region. The heating rate anomaly is similarly defined as the difference in heating rate from the heating rate in which AOT is less than 0.3. In this study, we do not consider the effects of clouds on this analysis, and all calculations are done for the clear-sky conditions. Heating anomalies thus calculated are “instantaneous,” which represent the heating anomalies instantaneously generated by the dust or dry anomaly before any atmospheric response.

Since this study is to estimate the climatological heating effect of dust, we compute the daily averages of the instantaneous heating rates. The daily averaged heating and flux anomalies are the averages of these quantities computed at 0000, 6000, 1200, and 1800 UTC by varying the solar zenith angle and the corresponding temperature and water vapor profiles. Since Aqua MODIS measures AOT approximately near 1330 LT, we assume the diurnal AOT variation is small and use the same AOT for our daily 4-time radiative transfer calculations.

We first consider the heating flux anomaly caused by dust alone (keeping temperature and moisture profiles at the background values). We applied Eq. (1) (Kaufman et al. 2005) to estimate the dust contribution to the observed AOT for August–September in 2003–06. The results are listed in Table 1. In the MDR, the dust contribution is more than 50% of the total AOT. Dust contributes the smallest in the western tropical Atlantic (i.e., region 3) for the total AOT range of 0–0.3, and the largest (∼92%) near the west coast of Africa (i.e., region 1) for the total AOT > 0.8. The CAM3’s dust content is in agreement with MODIS’s, with the largest difference in dust AOT of about 0.2 located in 20°–30°W when total AOT > 0.8. The CAM3’s dust in general contributes smaller fraction to the total AOT, with the largest difference in the dust percentage fraction to the MODIS’s of about 22% located in 20°–30°W when the total AOT < 0.3.

Figure 4 shows the vertical profiles of the models’ dust AOT that are used in our radiative transfer calculations. For this figure, the column-integrated AOT has been scaled to one. The altitude range of dust layers measured in CALIPSO is also indicated. Because we want to use the MATCH profile as a baseline profile, we lifted the MATCH dust AOT to match the CALIPSO dust layer range located between 600 and 800 hPa (∼2–4 km). This altitude range is also in agreement with aircraft measurements of dust vertical profiles by Léon et al. (2003) and Myhre et al. (2003). The CAM3 dust AOT peaks at lower altitudes (800–900 hPa). In general, CAM3 simulates a dust layer with wider vertical extent than the MATCH.

To estimate the effects of dust on the heating rate, we put dust profiles into Chou’s radiative transfer model. The input profile is based on the model’s simulated profile but with column-integrated dust AOT at 0.64 μm (a visible wavelength close to 0.55 μm at which MODIS reports its AOT) scaled to be equal to the dust AOT values estimated from the MODIS retrievals (listed in Table 1). In this way, we represent the vertical dust distributions for the various total AOT ranges shown in Figs. 2, 3. Although the MATCH AOT is obtained at a slightly different wavelength from that of MODIS AOT, direct measurements of dust AOT (e.g., Aerosol Robotic Network) at locations near the region of our interest (e.g., Cape Verde) indicate that the AOT at these two wavelengths differ by less than 10%. We also test that this difference does not significantly influence our results about the SAL heating property.

We first calculate dust radiative forcing at the surface and TOA over the MDR. The forcings are defined as the difference of net downward radiative fluxes for the various regional-averaged column-integrated dust AOT from the clear-sky flux. The results are summarized in Tables 3 and 4, in which we have also computed the forcing efficiency for dust aerosols, defined as the forcing divided by the column-integrated dust AOT. We see that the forcing efficiency is approximately region independent (in particular at the TOA), providing a means for intercomparison.

These results are generally consistent with recent calculations of dust forcings at the surface and the TOA over the North Atlantic (Evan et al. 2008; Myhre et al. 2003; Yoshioka et al. 2007). Our shortwave forcing is also generally consistent with that reported by Carlson and Benjamin (1980) and Zhu et al. (2007), although our longwave forcing is smaller than the forcing reported in either study.

On the basis of satellite observation of Saharan dust over the Atlantic Ocean, Li et al. (2004) and Christopher and Jones (2007) report diurnal-mean shortwave forcing efficiency at the TOA of −32 to −38 W m−2/AOT and −47.9 ± 3.81 W m−2/AOT in June–August, respectively. Our shortwave forcing efficiency at the TOA for August–September over the MDR is −30.0 W m−2/AOT for the MODIS–MATCH dust and −28.4 W m−2/AOT for the CAM3 dust, close to the value of –24.8 W m−2/AOT reported in Yoshioka et al. (2007) for June–August over the North Atlantic Ocean.

Christopher and Jones (2007) also reports an estimated longwave forcing efficiency of 8.96 ± 3.51 W m−2/AOT at the TOA, larger than our value of 4.30 (4.42) W m−2/AOT for the MATCH-MODIS (CAM3) dust and Yoshioka et al. (2007)’s value of 5.3 W m−2/AOT. We notice that our values of dust forcing are slightly different from those in Yoshioka et al. (2007), although we apply similar dust optical properties. The difference between our and their results may arise from the difference in the period and region being studied as well as the different radiative transfer models being used.

c. Radiative heating flux anomaly by the SAL

To estimate the total heating flux anomaly as a result of the SAL, we have to include both the dust and the dry anomaly. Figure 3 shows how the humidity of the SAL varies with the total AOT. Thus, it can be used with Table 1 to include both the dust and dry anomaly in the three subregions in our radiative transfer calculation. For example, for the SAL with the total AOT greater than 0.8 in region I, we include the corresponding dry anomaly (i.e., the thick-dashed line in Figs. 3a,d) together with the column-integrated dust AOT (0.98 or 0.78, see Table 1) in our heating flux anomaly calculation. The resulting changes in the daily averaged net downward radiative fluxes for this SAL perturbation are shown in Figs. 5c,d, together with the calculations including the dust-only in Figs. 5a,b for the MODIS–MATCH and CAM3 profiles, respectively.

For the dust-only case (Figs. 5a,b), the net downward shortwave flux is reduced by scattering and absorption, and the net downward longwave flux increases because less upward flux from the surface can reach above the dust layer (∼700–900 hPa) and more downward flux is emitted below the layer. The pattern of flux changes is similar for both MATCH-MODIS and CAM3 dust profiles, with the CAM3 calculation having smaller magnitude of shortwave flux changes because of fewer small-size particles.

In the SAL, both dust and dry anomalies change the radiative fluxes. Since water vapor is a greenhouse gas, the removal of water vapor in the lower troposphere reduces the greenhouse trapping of longwave radiation by water vapor, in turn modifying the radiative flux anomalies caused by dust. This is illustrated in Figs. 5c,d (the dashed–dotted line). Compared to the pure dust case, there is a decrease in the net downward longwave flux above the SAL because less water vapor increases the upward longwave radiation from the earth’s surface. Below the SAL, there is also a decrease in the net downward longwave flux (compared to the dust-only case) because of the reduced emission from the atmospheric water vapor. Since CAM3 simulates larger dry anomalies than the NCEP reanalysis (Fig. 3), the change of longwave flux in the CAM3 SAL is larger.

d. Radiative heating rate anomaly by dust and the SAL

The daily averaged heating anomaly can be calculated from vertical convergence of the daily averaged heating flux anomaly. Figures 6 –9 show the dust and the SAL radiative heating anomalies over the three regions in the MDR.

Absorption of shortwave radiation (SWR) by the dust heats the atmosphere around the dust layer (the dashed line in Figs. 6a, 7a). CAM3 has a lower-altitude dust layer and therefore the peaks of its shortwave heating anomalies are located at a lower altitude. The dust layer traps the longwave radiation (LWR) from the surface, cooling the atmosphere at and above the dust peak and warming the atmosphere below the peak (the dashed–dotted line in Figs. 6a, 7a). The net heating rate, therefore, has a peak below the dust concentration peak (∼600–800 hPa for the MODIS–MATCH and ∼800–900 hPa for the CAM3). The heating anomaly maximum is ∼0.2–0.3 K day−1, located between 850–900 hPa for the MODIS–MATCH, and ∼0.2–0.4 K day−1, near the surface for the CAM3. Carlson and Benjamin (1980) estimated the peak of the net heating rate of about 0.8 K day−1 located at 700 hPa. The difference between our and their results stems from their lower single-scattering albedo and more large particles than in our calculation (Yoshioka et al. 2007). Our heating rate anomaly profiles are similar to that computed by Zhu et al. (2007) for the Saharan coast, with most heating located at altitudes below 600 hPa.

Figures 6b–d, 7b–d show the net dust heating anomalies for regions 1–3 as a function of AOT for the MODIS–MATCH and the CAM3, respectively. The profiles of these heating anomalies do not resemble those of the temperature anomalies shown in Fig. 2. For example, the peaks of the heating rates in Figs. 6, 7 are not located at 850 hPa, where the peaks in temperature anomalies in Fig. 2 are located. Therefore, the radiative effect of dust alone cannot explain the temperature anomaly.

If we include the dry anomaly, the SAL heating anomalies for AOT > 0.8 are ∼0.1–0.4 K day−1 at 850 hPa (Figs. 8b–d, 9b). The dry anomaly reduces the longwave heating below 900 hPa compared to the pure dust case (comparing the dashed–dotted line in Fig. 8a to that in either Fig. 6a or Fig. 9a to that in Fig. 7a), because of the less downward emission from water vapor, and results in a maximum of heating at 850 hPa. The heating by the SAL at altitudes below 600 hPa implies that the dust and dry anomaly of the SAL together may help maintain its temperature inversion during its transport across the MDR.

There are drastic differences of the SAL heating rate anomalies in the middle troposphere (∼500 hPa) between the MODIS–MATCH and the CAM3. The CAM3 has positive heating rate anomalies around 500 hPa in regions 1 and 2, whereas the MODIS–MATCH has negligible anomalies there. Since radiative heating rate anomalies caused by pure dust do not show these drastic differences around 500 hPa (cf. Figs. 6, 7), differences in the water vapor profiles between the CAM3 and the NCEP dataset are likely the main cause of this difference.

e. Maintenance of the temperature inversion in the lower troposphere

The atmosphere tends to relax any temperature anomaly back to the background conditions through thermal radiation. With the heating caused by the dust and the dry anomalies in the lower troposphere, the cooling generated by thermal relaxation is compensated. Table 5 shows the instantaneous total cooling rate anomalies of the SAL (including dust, warm, and dry anomalies) at the top of the inversion layer as well as the thermal cooling rate anomalies (without dust and dry anomalies) at the same altitude. The top of the inversion layer is defined at the altitude of the maximum temperature anomaly in each case shown in Fig. 2: for the MODIS–MATCH cases, it is at 850 hPa for regions 1 and 2 and at 700 hPa for region III; for the CAM3 cases, it is at 850 hPa for almost all the cases but at 925 hPa for the case of AOT > 0.8 in region I.

In all regions, the SAL helps maintain its inversion layer by reducing thermal cooling rates. The thermal cooling rates are reduced by more than a factor of 2 in regions I and II for the MODIS–MATCH cases. In region III, the MODIS–MATCH SAL begins to lose its ability to maintain the thermal relaxation when AOT falls below 0.6. As shown in Fig. 8, the heating anomalies in 600–850 hPa are almost zero when AOT falls below 0.6. Since the heating anomalies from dust do not vary much with regions, the change in the SAL’s ability to maintain heat anomalies across the regions mainly arises from the change in the water vapor profiles. In CAM3, the peaks of water vapor anomalies are still located at 900 hPa for AOT in 0.4–0.6; therefore, the model’s SAL in region III still has the ability to maintain its inversion layer.

However, we also note that there is more uncertainty in the temperature, water vapor, and dust profiles in region III. The heating ability of the SAL in region III is still subject to questions. Further research is necessary to determine how dust and dry anomalies play a role in thermal property of the SAL in the western Atlantic.

4. Conclusions and discussion

Although many previous efforts have focused on the radiative effect of dust (Carlson and Benjamin 1980; Evan et al. 2008; Li et al. 2004; Myhre et al. 2003; Weaver et al. 2002; Yoshioka et al. 2007; Yu et al. 2006), this work is the first to quantify the relative role of dust and the associated dry anomalies in maintaining the atmospheric stability in the tropical cyclone main development region. Both dust and dry anomalies are important in heating up the SAL in the lower troposphere, with dust contributing about 50% of the maximum heating rate anomalies when AOT > 0.8. East of 40°W in the MDR, the dust and dry anomalies reduce thermal cooling at the top of the inversion layer and help maintain the inversion layer. West of 40°W, the SAL may lose its ability to maintain the inversion layer for AOT < 0.6. However, uncertainty in the dust, moisture, and temperature profiles in this area necessitate further investigation of this issue.

In the middle troposphere (∼500 hPa), where dust concentration is low, the SAL heating rate is sensitive to the local dry anomaly. The dryer midtroposphere therefore has a larger local heating rate anomaly. Thus, the CAM3 SAL has larger heating rate anomalies at 500 hPa than the MODIS-MATCH SAL. This may partially explain why the cold anomaly associated with the SAL at 500 hPa in the NCEP reanalysis temperature is much larger than that in the CAM3 simulation. The origin of the cold anomaly needs to be addressed in future investigations. Other diabatic heating processes may also play a role in generating this cold anomaly; for example, the suppressed deep convection associated with the SAL (Wong and Dessler 2005) may cause a reduction in latent heat release.

In this study, we have assumed the particle shape of dust is spherical, and we were able to use the Lorenz–Mie theory to compute the dust optical properties. Most recently, Yang et al. (2007) found that the nonsphericity effect of dust is essential to determine shortwave radiance at the top of the atmosphere. Further investigations are necessary to assess the sensitivity of the dust heating rates to the morphologies of dust particles. Moreover, our dust optical properties were derived from observations near the west coast of Africa. How dust optical properties change during its transport across North Atlantic Ocean is still an open question that needs further research. Finally, in this study, we did not consider feedbacks (e.g., changes in vertical motion and cloud cover) associated with the radiative heating anomaly. A more thorough investigation that includes feedbacks should involve experiments with a general circulation model coupled with dust transport and radiative properties (e.g., Yoshioka et al. 2007).

This study has indicated that several aspects of the SAL properties are missing in the CAM3 simulation. First, the missing cold anomalies in the CAM3 SAL imply that CAM3 simulates a dryer midtroposphere in the SAL. Second, the missing lifting of the lower tropospheric warm and dry anomalies in the CAM3 SAL while transported across the tropical Atlantic implies that the elevation of the inversion layer may not be appropriately simulated.

Given the discoveries of the negative trend of dust loading over the tropical North Atlantic (Foltz and McPhaden 2008; Wong et al. 2008), the effect of this decreasing dust loading on the long-term variation of the stability over the tropical North Atlantic requires further detailed investigations. As shown in this study, water vapor profiles are as important as the dust loading in assessing the heating rates in the dust layer. Therefore, the quality of current reanalysis, remote sensing retrievals, and model simulations of water vapor over dusty areas needs to be improved to accurately assess the heating rates in the dust layer.

Acknowledgments

We thank the NASA MODIS Science Team for the collection-5 product, NASA CALIPSO–CloudSat Science Team for the aerosol layer product, and their effort for making it easy to access the data from the LAADS and ASDC Web sites. We also thank Peter Colarco at NASA Goddard Space Flight Center for his detailed discussion of the optical properties of Saharan dust and two anonymous reviewers for their comments. This work is supported by NASA Grant NNX07AR12G to Texas A&M University.

REFERENCES

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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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Fig. 1.
Fig. 1.

August–September in 2003–06 climatology of total AOT for (a) Aqua MODIS at 0.55 μm and (b) CAM3 at 0.64 μm. The white area represents locations where no valid AOT retrievals were made. The dotted lines indicate the TC MDR divided into three subregions (I, II, and III).

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 2.
Fig. 2.

(a)–(c) NCEP temperature profile anomalies (K) over the TC MDR as a function of MODIS AOT ranges for August–September in 2003–06. (d)–(f) Same as (a)–(c) but for the CAM3 temperature anomalies as functions of the CAM3 AOT. The anomalies are relative to the temperature profiles with the smallest AOT range (0–0.3).

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 3.
Fig. 3.

Similar to Fig. 2 but for specific humidity profile anomalies (g kg−1).

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 4.
Fig. 4.

Climatology of the (a) MATCH and (b) CAM3 dust vertical profiles over three subregions in the TC MDR for August–September in 2003–06. The dust concentrations are given as AOT per 20 hPa. The dotted horizontal lines in (a) show the altitude range of dust layers indicated by the CALIPSO version 2.01 layer data over the MDR. The MATCH dust profile is lifted by 100 hPa so that the dust concentration peak is within this altitude range. The vertically integrated AOTs are scaled to one for both plots.

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 5.
Fig. 5.

Changes in net downward radiative fluxes (W m−2) by the (a) MODIS–MATCH dust, (b) CAM3 dust, (c) MODIS–MATCH SAL (both dust and dry anomaly), and (d) CAM3 SAL over region 1 for the cases of column-integrated AOT > 0.8 (see Table 1) for August–September in 2003–06. The solid lines are the changes in total fluxes, the dashed lines are the changes in shortwave fluxes, and the dashed–dotted lines are changes in longwave fluxes.

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 6.
Fig. 6.

(a) Climatology of heating rate anomalies (K day−1) caused by the MODIS–MATCH dust of column-integrated AOT of one for August–September in 2003–06 over region 1. The total heating rate anomaly (solid line) is decomposed into shortwave (dashed line) and longwave (dashed–dotted line) heating anomalies. Total heating rate anomalies (K day−1) as functions of column-integrated AOT for (b) region 1, (c) region 2, and (d) region 3.

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 7.
Fig. 7.

Same as Fig. 6 but for the heating rate anomalies caused by the CAM3 dust.

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 8.
Fig. 8.

Same as Fig. 6 but for the heating rate anomalies caused by the MODIS–MATCH SAL (both dust and dry anomaly).

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Fig. 9.
Fig. 9.

Same as Fig. 7 but for the heating rate anomalies caused by the CAM3 SAL (both dust and dry anomaly).

Citation: Journal of Climate 22, 19; 10.1175/2009JCLI2847.1

Table 1.

Averaged dust AOT contents in the various MODIS (at 0.55 μm) and CAM3 (at 0.64 μm) total AOT ranges over the TC MDR for August–September in 2003–06. The numbers in the parentheses show the percentage content of dust in the total AOT.

Table 1.
Table 2.

Single-scattering albedos of dust in the visible wavelength bands used in the radiative transfer model as a function of dust size bin.

Table 2.
Table 3.

Radiative forcing (W m−2) by the MODIS–MATCH dust at the surface and TOA over the TC MDR for August–September in 2003–06. The column-integrated dust AOT at 0.55 μm for each subregion and the whole MDR is used for the calculations. Forcing efficiency is the forcing divided by the corresponding regional-averaged dust AOT.

Table 3.
Table 4.

Same as Table 3 but for the CAM3 dust. The column-integrated dust AOT at 0.64 μm for each subregion and the whole MDR is used for the calculations.

Table 4.
Table 5.

The MODIS–MATCH SAL and CAM3 SAL total heating rates (K day−1), including dust, dry, and warm anomalies at the top of the inversion layer. The numbers in the parentheses are the heating rates without the dust and dry anomalies. The top of the inversion layer is defined at the altitude of the maximum temperature anomaly in Fig. 2. For MODIS–MATCH, it is at 850 hPa for regions I and II and at 700 hPa for region III. For CAM3 it is at 850 hPa for almost all cases, except for AOT > 0.8 in region I, it is at 925 hPa.

Table 5.
Save
  • Carlson, T. N., and J. M. Prospero, 1972: The large-scale movement of Saharan air outbreaks over the northern equatorial Atlantic. J. Appl. Meteor., 11 , 283297.

    • Search Google Scholar
    • Export Citation
  • Carlson, T. N., and S. G. Benjamin, 1980: Radiative heating rates for Saharan dust. J. Atmos. Sci., 37 , 193213.

  • Chou, M-D., and M. J. Suarez, 2002: A solar radiation parameterization for atmospheric studies. Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-1999-104606/VOL15, 38 pp.

    • Search Google Scholar
    • Export Citation
  • Chou, M-D., M. J. Suarez, X-Z. Liang, and M. M-H. Yan, 2003: A thermal infrared radiation parameterization for atmospheric studies. Tech. Rep. Series on Global Modeling and Data Assimilation, NASA/TM-2001-104606/VOL19, 68 pp.

    • Search Google Scholar
    • Export Citation
  • Christopher, S. A., and T. Jones, 2007: Satellite-based assessment of cloud-free net radiative effect of dust aerosols over the Atlantic Ocean. Geophys. Res. Lett., 34 , L02810. doi:10.1029/2006GL027783.

    • Search Google Scholar
    • Export Citation
  • Colarco, P. R., O. B. Toon, O. Torres, and P. J. Rasch, 2002: Determining the UV imaginary index of refraction of Saharan dust particles from Total Ozone Mapping Spectrometer data using a three-dimensional model of dust transport. J. Geophys. Res., 107 , 4289. doi:10.1029/2001JD000903.

    • Search Google Scholar
    • Export Citation
  • Colarco, P. R., O. B. Toon, and B. N. Holben, 2003: Saharan dust transport to the Caribbean during PRIDE: 1. Influence of dust sources and removal mechanisms on the timing and magnitude of downwind aerosol optical depth events from simulations of in situ and remote sensing observations. J. Geophys. Res., 108 , 8589. doi:10.1029/2002JD002658.

    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and Coauthors, 2006: The formulation and atmospheric simulation of the Community Atmosphere Model Version 3 (CAM3). J. Climate, 19 , 21442161.

    • Search Google Scholar
    • Export Citation
  • Dubovik, O., and Coauthors, 2002: Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci., 59 , 19591966.

    • Search Google Scholar
    • Export Citation
  • Dunion, J. P., and C. S. Velden, 2004: The impact of the Saharan air layer on Atlantic tropical cyclone activity. Bull. Amer. Meteor. Soc., 85 , 353365.

    • Search Google Scholar
    • Export Citation
  • Dunion, J. P., and C. S. Marron, 2008: A reexamination of the Jordan mean tropical sounding based on awareness of the Saharan air layer: Results from 2002. J. Climate, 21 , 52425253.

    • Search Google Scholar
    • Export Citation
  • Evan, A. T., J. Dunion, J. A. Foley, A. K. Heidinger, and C. S. Velden, 2006: New evidence for a relationship between Atlantic tropical cylone activity and African dust outbreaks. Geophys. Res. Lett., 33 , L19813. doi:10.1029/2006GL026408.

    • Search Google Scholar
    • Export Citation
  • Evan, A. T., and Coauthors, 2008: Ocean temperature forcing by aerosols across the Atlantic tropical cyclone development region. Geochem. Geophys. Geosyst., 9 , Q05V04. doi:10.1029/2007GC001790.

    • Search Google Scholar
    • Export Citation
  • Foltz, G. R., and M. J. McPhaden, 2008: Trends in Saharan dust and tropical Atlantic climate during 1980–2006. Geophys. Res. Lett., 35 , L20706. doi:10.1029/2008GL035042.

    • Search Google Scholar
    • Export Citation
  • Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nuñez, and W. M. Gray, 2001: The recent increase in Atlantic hurricane activity: Causes and implications. Science, 293 , 474479.

    • Search Google Scholar
    • Export Citation
  • Hand, J., N. Mahowald, Y. Chen, R. Siefert, C. Luo, A. Subramaniam, and I. Fung, 2004: Estimates of atmospheric-processed soluble iron from observations and a global mineral aerosol model: Biogeochemical implications. J. Geophys. Res., 109 , D17205. doi:10.1029/2004JD004574.

    • Search Google Scholar
    • Export Citation
  • Jones, C., N. Mahowald, and C. Luo, 2004: Observational evidence of African desert dust intensification of easterly waves. Geophys. Res. Lett., 31 , L17208. doi:10.1029/2004GL020107.

    • Search Google Scholar
    • Export Citation
  • Kanamitusu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83 , 16311643.

    • Search Google Scholar
    • Export Citation
  • Karyampudi, V. M., and T. N. Carlson, 1988: Analysis and numerical simulations of the Saharan air layer and its effect on easterly wave disturbances. J. Atmos. Sci., 45 , 31033136.

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  • Fig. 1.

    August–September in 2003–06 climatology of total AOT for (a) Aqua MODIS at 0.55 μm and (b) CAM3 at 0.64 μm. The white area represents locations where no valid AOT retrievals were made. The dotted lines indicate the TC MDR divided into three subregions (I, II, and III).

  • Fig. 2.

    (a)–(c) NCEP temperature profile anomalies (K) over the TC MDR as a function of MODIS AOT ranges for August–September in 2003–06. (d)–(f) Same as (a)–(c) but for the CAM3 temperature anomalies as functions of the CAM3 AOT. The anomalies are relative to the temperature profiles with the smallest AOT range (0–0.3).

  • Fig. 3.

    Similar to Fig. 2 but for specific humidity profile anomalies (g kg−1).

  • Fig. 4.

    Climatology of the (a) MATCH and (b) CAM3 dust vertical profiles over three subregions in the TC MDR for August–September in 2003–06. The dust concentrations are given as AOT per 20 hPa. The dotted horizontal lines in (a) show the altitude range of dust layers indicated by the CALIPSO version 2.01 layer data over the MDR. The MATCH dust profile is lifted by 100 hPa so that the dust concentration peak is within this altitude range. The vertically integrated AOTs are scaled to one for both plots.

  • Fig. 5.

    Changes in net downward radiative fluxes (W m−2) by the (a) MODIS–MATCH dust, (b) CAM3 dust, (c) MODIS–MATCH SAL (both dust and dry anomaly), and (d) CAM3 SAL over region 1 for the cases of column-integrated AOT > 0.8 (see Table 1) for August–September in 2003–06. The solid lines are the changes in total fluxes, the dashed lines are the changes in shortwave fluxes, and the dashed–dotted lines are changes in longwave fluxes.

  • Fig. 6.

    (a) Climatology of heating rate anomalies (K day−1) caused by the MODIS–MATCH dust of column-integrated AOT of one for August–September in 2003–06 over region 1. The total heating rate anomaly (solid line) is decomposed into shortwave (dashed line) and longwave (dashed–dotted line) heating anomalies. Total heating rate anomalies (K day−1) as functions of column-integrated AOT for (b) region 1, (c) region 2, and (d) region 3.

  • Fig. 7.

    Same as Fig. 6 but for the heating rate anomalies caused by the CAM3 dust.

  • Fig. 8.

    Same as Fig. 6 but for the heating rate anomalies caused by the MODIS–MATCH SAL (both dust and dry anomaly).

  • Fig. 9.

    Same as Fig. 7 but for the heating rate anomalies caused by the CAM3 SAL (both dust and dry anomaly).

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