JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D16, 4289, /2001JD000903, 2002

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D16, 4289, /2001JD000903, 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 Peter R. Colarco and Owen B. Toon Laboratory for Atmospheric and Space Physics, Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado, USA Omar Torres Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, Maryland, USA Philip J. Rasch National Center for Atmospheric Research, Boulder, Colorado, USA Received 1 June 2001; revised 26 November 2001; accepted 30 November 2001; published 24 August [1] A three-dimensional model has been developed for simulating Saharan dust emissions and transport over the tropical North Atlantic Ocean. The computed dust fields are constrained by data from ACE-2, and we use a radiative transfer code to simulate the Total Ozone Mapping Spectrometer on the Earth Probe satellite (EP-TOMS) aerosol index (AI). Using the observed relationship between AI and aerosol optical depth, we determine from our simulations the UV refractive index for dust particles at Dakar, Sal, and Tenerife. We find that the dust imaginary refractive index at Sal and Tenerife is approximately k = ( ) at 331 nm and k = ( ) at 360 nm. At Dakar the dust imaginary refractive index is approximately k = ( ) at 331 nm and k = ( ) at 360 nm. These values are considerably less absorbing than the refractive index currently used in the TOMS retrievals of dust optical depth and single scatter albedo. Once the dust refractive index has been constrained, we calculate the single scatter albedo by integrating across the particle size distribution. We find that the particle single scatter albedo at 331 nm is v 0 = 0.81 ( ) at Dakar, v 0 = 0.84 ( ) at Sal, and v 0 = 0.86 ( ) at Tenerife. The refractive index determined in this study should be useful for future retrievals using the TOMS data, as well as for energy balance studies that incorporate the radiative effects of mineral dust aerosols. INDEX TERMS: 0368 Atmospheric Composition and Structure: Troposphere constituent transport and chemistry; 3360 Meteorology and Atmospheric Dynamics: Remote sensing; 3359 Meteorology and Atmospheric Dynamics: Radiative processes; KEYWORDS: dust aerosols, transport model, radiative properties, satellite retrieval, single scatter albedo, imaginary refractive index 1. Introduction [2] Mineral dust aerosols are important to Earth s radiative budget and climate [Intergovernmental Panel on Climate Change (IPCC ), 1995]. Dust is a major component of the total tropospheric aerosol burden; current estimates of the global source strength are in the range of Tg yr 1 [e.g., Andreae, 1995], with most of this flux available for long-range transport. Dust particles interact with sunlight by absorbing and scattering radiation [Sokolik and Toon, 1996; Tegen et al., 1996], perturbing the heating rates at the surface and within the atmosphere. Deposition of dust to terrestrial and oceanic surfaces is important in the nutrient supply and productivity of the associated ecosystems [Duce et al., 1991; Swap et al., 1992]. Mineral dust is potentially Copyright 2002 by the American Geophysical Union /02/2001JD important as a surface for chemical reactions to occur on [Dentener et al., 1996]. Dust may also influence Earth s radiative budget through the modification of cloud properties [Wurzler et al., 2000]. Additionally, mineral dust aerosols can affect satellite retrievals of various geophysical parameters (e.g., atmospheric ozone) [Torres and Bhartia, 1999]. Unfortunately, assessing these effects is difficult because dust radiative properties are generally poorly known [Sokolik and Toon, 1999]. [3] In this paper we address dust radiative properties in the context of a numerical simulation of Saharan dust emissions during the Second Aerosol Characterization Experiment (ACE-2), which was based out of the Canary Islands during June and July 1997 [Raes et al., 2000]. We have chosen this case study because of the availability of the ACE-2 data set, which provides dust vertical and particle size distributions near Tenerife, and the availability of satellite and Sun photometer observations of Saharan dust AAC 4-1

2 AAC 4-2 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION during this time period. Our approach is to constrain our dust transport model with the ACE-2 data, thereby lessening the sensitivity of our results to the details of the dust source formulation. We then perform radiative transfer calculations on the modeled dust fields in order to simulate the UV radiance observed by the Total Ozone Mapping Spectrometer on the Earth Probe satellite (EP-TOMS). The TOMS instrument has been used to develop an aerosol index (AI) for qualitatively detecting the presence of UV absorbing aerosols over both land and ocean surfaces [Herman et al., 1997]. A quantitative relationship between the TOMS measured radiances and aerosol properties is desired, but such an analysis is complicated by uncertainties in the aerosol height and radiative properties [Torres et al., 2002a]. Hsu et al. [1999] determined the ratio between the EP-TOMS AI and the aerosol optical depth (AOD) for dust aerosols over the tropical North Atlantic Ocean during summer This ratio is approximately independent of dust optical depth, so that comparisons with a transport model are relatively insensitive to the model s dust source strength. By testing the sensitivity of our calculated ratio of AI and optical depth to variations in the dust imaginary refractive index we can determine the radiative properties that allow our model to best match the observations. Once the dust refractive index has been constrained, we calculate the single scatter albedo by integrating across the particle size distribution. The dust single scattering albedo is important to constraining assessments of the climatic effects of dust particles, while the refractive indices retrieved by this technique may aid in future satellite inversions. [4] Section 2 describes the dust model we have developed. Section 3 discusses the transport model calculations in the context of the ACE-2 data set. Section 4 describes the radiative transfer calculations we have carried out and our comparison to TOMS observations. Section 5 discusses our results and suggests future applications of this work. 2. Model Description 2.1. Dynamical Module [5] Our dust model uses winds, diffusion coefficients, and cloud fields from the National Center for Atmospheric Research ( NCAR) Model for Atmospheric Transport and Chemistry (MATCH). MATCH is an offline chemical transport model driven by the meteorological fields archived in the National Center for Environmental Prediction (NCEP) reanalysis package [Rasch et al., 1997]. The NCEP reanalysis fields are available for each day at 0000, 0600, 1200, and 1800 UTC. MATCH is run with an 1800 second time step, and it interpolates the input fields to the current step and diagnoses planetary boundary layer transport, convective mixing, and cloud and precipitation fields using the same basic physics package employed by the NCAR Community Climate Model version 3 (CCM3). We run MATCH at the same spatial resolution as the NCEP reanalyses: T63 horizontal resolution (approximately ) with 28 vertical sigma layers extending to 35 km Aerosol Transport Module [6] The global dynamical fields generated by MATCH are exported to our aerosol module, a version of the Figure 1. The horizontal domain of our model of Saharan dust emissions and transport over the North Atlantic Ocean. The colored area over North Africa is the domain of our dust source model [Marticorena et al., 1997], with the colors indicating the minimum 10-m wind speed for dust emissions at 1 1 horizontal resolution. Labeled points indicate AERONET Sun photometer sites used in this study. ACE-2 ground-based measurement sites referenced in this paper are on Tenerife (lidars and Sun photometers). The referenced aircraft flights were based out of Tenerife and took place in its immediate vicinity. See color version of this figure at back of this issue. University of Colorado/NASA Ames Community Aerosol and Radiation Model for Atmospheres (CARMA) [Toon et al., 1988]. CARMA is run at the same temporal and spatial resolution as MATCH, but on a limited area grid. The horizontal domain of the model is shown in Figure 1. Since we are interested in tropospheric dust transport, in order to improve computational efficiency we restrict the vertical grid to the lowest 21 model layers (extending to approximately 15 km). [7] CARMA contains our prescriptions of the aerosol source, transport, and removal terms. The particle size distribution is treated using a number of discrete bins distributed in radius space, with the physical processes affecting each bin handled independently. Advective and diffusive transport are calculated using a piecewise parabolic scheme following Lin and Rood [1996], and allows a fully implicit solution in cases where a Courant number of unity is exceeded (typically only relevant for sedimentation of large particles in the lowest model layers). Sedimentation is accounted for by treating the particles as solid spheres and adding the fall velocity at each radius bin to the vertical wind component in the relevant advective calculation. A deposition velocity is calculated for the lowest model layer accounting for sedimentation, diffusion, and turbulent deposition to the surface [Seinfeld and Pandis, 1998]. A wet removal scheme is in place in the model, but we neglect that process for this study, where we are only looking at aerosols near the African coast Dust Source Module [8] The dust source module is from Marticorena and Bergametti [1995], which takes as input the surface soil size distribution, roughness length, distribution of nonerodible elements, and the 10-m wind speed on a 1 1 horizontal grid. The threshold friction velocity for dust emissions is

3 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION AAC 4-3 Table 1. Mass Modal Radii r, Standard Deviation s, and Mass Percentage p for Size Distributions Discussed in This Study a r 1 s 1 p 1 r 2 s 2 p 2 r 3 s 3 p 3 r eff Colarco de Reus e Schulz e Torres Sulfate Sea Salt a Also included are the optical effective radii [mm] of each distribution. The mass modal radii are in micrometers. "Colarco" is the modeled dust particle size distribution used in this study (derived from Cautenet et al. [2000]). de Reus is the dust particle size distribution from the parameters measured by de Reus et al. [2000]. Schulz is the dust particle size distribution used in the model of Schulz et al. [1998]. Torres is the dust particle size distribution D2 identified in Torres et al. [1998]. Sulfate and Sea Salt are the respective nonabsorbing aerosol distributions used by Torres et al. [1998]. calculated from these parameters using the formulation of Iversen and White [1982] as modified for the increase in roughness length due to the presence of a dust saltation layer [Gillette et al., 1998]. The horizontal dust saltation flux is calculated according to White [1979], and the vertical dust flux is computed from that according to the fractional clay content of the soil. This model is applied to a surface property map of the Sahara desert. The dust emission frequencies calculated with the 1 1 ECMWF 10-m wind fields were validated against infrared satellite imagery for the years 1991 and 1992 [Marticorena et al., 1997]. The vertical dust fluxes produced in the Marticorena and Bergametti model are for emitted particles smaller than 10 mm in radius. [9] We have computed the minimum 10-m wind speed needed for dust emissions over the source region; this threshold velocity is greater than 7.5 m s 1 at all points in the source region (Figure 1). Dust fluxes have been computed using several different methods, in all cases using the 6-hourly input wind fields and assuming those fluxes are representative over that time interval. Using the ECMWF wind fields we determine total emissions of particles of radius less than 10 mm is 42.2 Tg during July This value can be compared with the results presented in Marticorena et al. [1997], where the computations were done over a somewhat smaller source region; there the values were computed to be 60.6 Tg and 65.1 Tg for July 1991 and 1992 respectively. [10] The ECMWF wind fields are not generally available to us, and so we have adapted the dust module to the relatively lower resolution NCEP 10-m wind fields. The total emissions calculated by interpolating the NCEP winds to the 1 1 horizontal grid are 4.1 Tg for July The large discrepancy in the emissions computed with the NCEP and ECMWF wind fields can in part be attributed to the more frequent occurrence of high wind speed events in the ECMWF data set than in the NCEP data set. For each 6- hour wind input interval during July 1997 we counted the number of grid points over the source region which exceeded 7.5 m s 1 in wind speed; there were approximately 8000 such points for the ECMWF data set versus 2,200 for the NCEP data set. The maximum wind speed over the source region was also typically 1 to 2 m s 1 greater in the ECMWF set at each time interval. Since the vertical dust flux goes as the third power of the wind speed, the generally lower wind speeds present in the NCEP data set have the effect of dramatically reducing the calculated dust flux relative to what a higher resolution input data set would predict. [11] To increase the dust emissions produced with the NCEP wind set we have followed Westphal et al. [1988] in assuming that the actual surface wind speed subgridscale and subtimescale variability can be represented by a Rayleigh probability distribution function spread about the input surface wind speed. The functional form of this distribution is n o gu ð 0 ;UÞ ¼ 2U0 U 2 exp ð U0 =UÞ 2 where U is the input surface wind speed and U 0 is the subscale variability in the wind speed. This distribution has an exponentially decaying high-wind speed tail. The total emission is computed by integrating the function in equation (1) across the dust fluxes computed for a range of U 0 values. In practice, we compute this integral for 0 m s 1 < U 0 < 40 m s 1. Surface winds speeds as high as 40 m s 1 are unrealistic, but the shape of the probability distribution function makes the importance of those winds negligible. For the NCEP input winds used in our model, 90% of the total dust emission is contributed from subscale winds U 0 <20ms 1. The total emissions of particles of radius less than 10 mm calculated by this method is Tg for July This value is about three times larger than what was calculated using the ECMWF wind fields and what is reported by Marticorena et al. [1997]. [12] The dust source module does not predict the emitted dust particle size distribution, so we assume a wind speed and location independent initial size distribution. We partition the lifted mass across a three-mode lognormal size distribution that was derived from Cautenet et al. [2000] (Table 1). They do not provide a complete description of the size distribution they used, incorrectly referring to it as d Almeida s [1987] Wind Carrying Dust distribution. We used the modal radii and mass fraction reported in Cautenet et al. [2000] and associated those with the size distribution standard deviations applicable to d Almeida s [1987] Sandstorm model. The emitted dust flux is partitioned across 30 discrete bins spaced logarithmically in radius from 0.01 to 50 mm. Approximately 45.7% of the mass of this size distribution lies in particles smaller than 10 mm in radius, and 42.6% of the mass lies in particle with radii between 10 and 50 mm. Accordingly, we scale the emission fluxes computed by the Marticorena and Bergametti model by a factor of 1.93 in order to account for the large particle end of the modeled size distribution that extends beyond 10 mm. [13] There are many issues related to our use of the NCEP wind fields and our choice of the initial size ð1þ

4 AAC 4-4 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION Figure 2. Five-day back trajectories calculated with the NCEP reanalyses for Tenerife, Dakar, and Sal. Trajectories end at the indicated locations on 8, 15, and 17 July respectively. The first three figures at each location are for the 300 K potential temperature surface (approximately 1 km asl). The remaining figures at each location are for the 320 K potential temperature surface (approximately 4 km asl).

5 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION AAC 4-5 distribution for the emitted aerosols. In particular, our choice of the surface wind field used to calculate dust emissions needs to be validated. We noted earlier that the maximum wind speed over the source region was typically 1to2ms 1 greater in the ECMWF wind set than in the NCEP data. Possibly the NCEP data suffers from an easily corrected low bias. A consequence of applying the wind distribution function in equation (1) is that most surface grid cells in the source region emit dust even for mean surface wind speeds of only a few m s 1, well below the threshold values indicated in Figure 1. We do not attempt a rigorous justification of our approach here since we are not trying to validate the dust sources. For purposes of this study, we are satisfied to obtain good agreement between the modeled and measured optical depths and particle size distributions at places where we have optical data available. We point out, however, that the large total emissions predicted with this method are mitigated by the high deposition due to the large mass modal radii that account for the majority of the emitted dust. Additionally, we have run our transport model using the fluxes calculated with the ECMWF winds and assuming a size distribution as in the work of Schulz et al. [1998] (Table 1), but while the agreement between the measured and modeled optical depths was good, the agreement between the measured and modeled particle size distributions was generally poor. Further comparison of these different methods is beyond the scope of this study. Validation of the source calculation and the size distribution of the emitted dust is the subject of ongoing research. 3. Comparison of Model With Data From ACE-2 [14] We simulate dust lifting and transport during the Second Aerosol Characterization Experiment (ACE-2), which was organized by the International Global Atmospheric Chemistry project (IGAC) [Raes et al., 2000]. The intensive phase of ACE-2 took place from 16 June to 24 July 1997, over the subtropical North-East Atlantic Ocean. The most intensive measurements were made between the Canary Islands and Sagres (southern Portugal). Additional measurements were made with the Sun photometers in the AERO- NET network (identified in Figure 1) and with space-based remote sensing platforms like AVHRR and EP-TOMS Patterns of Dust Transport [15] In Figure 2 we illustrate select air trajectories for Tenerife (Canary Islands, N, W), Dakar (Senegal, N, W), and Sal (Cape Verde Islands, N, W) during ACE-2, where 5-day back-trajectories have been calculated from NCEP reanalyses using the NASA Goddard FTRAJ package. Figures 2a, 2b, and 2c show the back trajectories to Tenerife on 8, 15, and 17 July at the 300 K isentropic surface (approximately 900 hpa or 1 km above sea level (asl)). The influence on the marine boundary layer (MBL) at the Canaries from both maritime and European sources is evident, and none of the trajectories indicate transport from Africa. Higher altitude trajectories are shown in Figures 2d, 2e, and 2f, which are for 8, 15, and 17 July at the 320 K isentropic surface (approximately 600 hpa or 4 km asl). On 15 July the transport in the free troposphere at the Canaries is from the north, so there is no possibility of dust transport, while on 8 and 17 July the transport is from Africa and dust was observed over the islands [Formenti et al., 2000]. The trajectories further indicate that the dust would come from different sources in these two cases, coming more from the north on 8 July and from the south on 17 July. In a similar manner, the low altitude air trajectories at Dakar and Sal are characterized by coastal flow. The higher altitude air is from over Africa, though it is evident that dust observed at Tenerife, Dakar, and Sal might each have different origins. [16] In Figure 3 we compare the horizontal extent of the modeled dust distribution to satellite observations made by the NOAA-14 AVHRR and the Earth Probe TOMS sensors. We have calculated the aerosol optical depth from our computed dust fields assuming Mie scattering and a visible wavelength refractive index of n = i [after Moulin et al., 1997]. The modeled optical depth is compared with the AVHRR 1 1 single channel (l = 630 nm) aerosol optical depth retrieval, which assumes a simple Junge aerosol size distribution and a nonabsorbing refractive index [Husar et al., 1997]. The retrieval assumptions made by AVHRR are not generally representative of dust, and so we offer only a semiquantitative comparison for purposes of determining the position of the dust plume. Additionally, because of the generally high value of the land surface reflectance at visible wavelengths, AOD retrievals are only made over the darker ocean surface. Also shown is a comparison of the modeled column mass with the EP-TOMS AI. The AI is useful to detect the presence of UV absorbing aerosols over both land and ocean because the surface reflectance is uniformly low in the UV. The AI is increasingly positive in proportion to increasing aerosol optical depth and height, but is not generally sensitive to aerosols in the lowest 1 km of the atmosphere [Herman et al., 1997] (we show the modeled column mass for aerosols above 1 km altitude). Because of the AVHRR cloud mask and the lack of daily global coverage with both sensors, we have averaged the model and observations over a 5-day period for this comparison. [17] Figure 3 shows the modeled dust plume extending over the Atlantic at around 15 N and extending over both Sal and Dakar, consistent with the location identified by both sensors. The AVHRR observations show high optical depths (0.5) near the west African coast south of Tenerife, and the model and the EP-TOMS observations show that Tenerife is on the northwestern edge of the dust transport path. Both AVHRR and EP-TOMS detect a dust plume extending over the west-central Mediterranean (north and east of Tunisia) that is also present in the model. EP-TOMS shows near-zero AI values in the northeastern Atlantic and in a band over Africa between 5 and 10 N, in apparent contrast with the model, but we note that measurements of UV reflectivity (not shown) identify these areas as having persistent cloud cover, which tends to produce small AI values. We further note that the high AOD and AI detected by the satellite sensors off the west African coast south of the equator are presumably biomass-burning aerosols which are not included in our model Model Validation at Tenerife Ground-Based Radiation Measurements [18] The majority of the data available to us for validating our model is from the ACE-2 measurement sites in the Canary Islands. A shadow-band radiometer

6 AAC 4-6 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION a) b) Figure 3. Comparison of the modeled horizontal distribution of dust to observations by EP-TOMS and AVHRR. We have averaged the model and satellite data over a five-day period to reduce the data dropout. The panel at left shows a comparison of the modeled AOD at 630 nm to the AVHRR retrieval. The panel at right shows a comparison of the modeled column mass of dust at altitudes above 1 km to the EP- TOMS AI. and Sun photometer located at Mount Teide (3570 m asl, on Tenerife) identified three distinct dust episodes over the Canary Islands during ACE-2: 8 9 July, July, and July [Formenti et al., 2000]. During these episodes, the radiometer-measured aerosol optical depths (AOD) at 500 nm were observed to increase from (background conditions) to as high as (Figure 4). The Ångstrom exponent (derived from the 500 nm and 415 nm AOD measurements) was in the range 0.17 to 0.63 for these high optical depth periods. Inversion of the radiometer AOD measurements yielded columnar size distributions for the dust aerosols [following King et al., 1978], which were characterized by a volume mode peak between 4 8 micrometers in diameter (with the exception of the 18 July retrieval, which yielded a volume size distribution peaked at about 0.5 mm and hence the largest Ångstrom exponent value). [19] Two Sun photometers were also stationed on Tenerife as part of the AERONET network. These instruments provide ground-based aerosol optical depth retrievals at seven wavelengths between 340 and 1020 nm [Holben et al., 1998]. Quality assured and cloud-screened data are available during ACE-2 both at a sea level site and at the Izana mountain top observatory (2370 m asl). A limited

7 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION AAC 4-7 lower diameter than was retrieved, but the agreement between the model and observations for particles less than 2 mm in diameter, where interactions with solar radiation are most important, is quite satisfying. [21] Two sets of lidar measurements were made from different altitudes on Tenerife during ACE-2. For various reasons, data are available only for the second dust event (17 18 July). The micropulse lidar (MPL) at the Izana observatory (2370 m asl) first noted the appearance of dust at 2200 UTC on 16 July at an altitude of approximately 3.5 km. The dust was observed to drop in altitude on 17 July and remained mostly confined between 2.5 and 4 km for the duration of the event, during which time the 523 nm optical depth of the layer varied between 0.16 and 0.23 [Welton et al., 2000]. Concurrent measurements with the MPL stationed at Las Galletas (at sea level, also on Tenerife) probed Figure 4. Aerosol optical depth comparisons for three different altitudes on Tenerife. The bottom figure compares the modeled column optical depth at 670 nm to that observed at the AERONET site at sea level. The middle figure corresponds to the measured and modeled 670 nm optical depths above the altitude of the AERONET site at the Izana observatory (2370 m asl). The upper figure compares the modeled 500 nm optical depth to data from Formenti et al. [2000] for aerosols above the altitude of Mount Teide (3570 m asl). a) number of column integrated particle size distributions are available from inversion of sky radiance measurements made at a number of scattering angles [Dubovik and King, 2000]. [20] Figure 4 shows a comparison of computed aerosol optical depth at 670 nm to the retrieved optical depth at the Tenerife (sea level) and Izana (2370 m) AERONET sites and the 500 nm AOD at the Mount Teide (3570 m) observatory. The model reproduces the observed temporal variability in the optical depth, capturing the three identified dust passages. The magnitude of the modeled AOD averages 30% error compared to the Izana measurements and 15% error compared to the Tenerife measurements. The offset between the model and sea level data at times of low dust loading is due to marine boundary layer aerosols not included in the model. In Figure 5 we compare the modeled column integrated size distribution for points above 2370 m to that retrieved from the Izana stationed Sun photometer on 8 July. The modeled size distribution is peaked at a slightly b) Figure 5. Comparison of modeled size distributions near Tenerife to observations. Here we are directly comparing the model to the observations without normalizing the particle size distributions. The top figure shows the column integrated size distribution from the model compared to AERONET observations at Izana (2370 m asl). The bottom figure shows a comparison of the modeled dust particle size distribution to size distributions measured on aircraft at approximately 3500 m asl, 8 July The solid line is the modeled distribution. The dashed line is a fit to the de Reus et al. [2000] measurements. The dotted line is the CIRPAS measured size distribution [Collins et al., 2000].

8 AAC 4-8 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION Figure 6. Comparison of the modeled vertical extinction profile to the profile measured by the micropulse lidar at Tenerife, 17 July 1997 [Powell et al., 2000]. both the MBL air and the dust layer in the free troposphere [Powell et al., 2000]. For the July dust episode the optical depth of the dust layer varied between 0.1 and 0.3. Comparisons of the Las Galletas and Izana MPL data show that although there is a 40 km distance and significant topographical difference between the two sites, the dust layer optical depth above the height of Izana is approximately the same at both sites (t ). Figure 6 shows a comparison of the modeled extinction profile to the profile observed by the Las Galletas MPL for the 17 July event. The retrieved extinction profile shows the dust layer extending from about 1900 m to 5000 m with peak extinction at about 3000 m. The modeled profile is peaked at about 1.5 km and the total extinction is too low at low altitudes. We note that in Figure 4 the modeled optical depth is too low compared to the Sun photometer observations from the surface for this event due to the model s neglect of marine boundary layer aerosols, but that the agreement improves at higher altitudes Aircraft Measurements [22] Flights of the Center for Interdisciplinary Remotely Piloted Aircraft Studies (CIRPAS) Pelican aircraft encountered dust laden air in the free troposphere near Tenerife on 8 July and 17 July. Measurements of the particle size distributions were made by concatenating the results of observations with the Caltech Automated Classified Aerosol Detector (a differential mobility analysis system) and two optical particle counters, yielding size distributions out to about 8 micrometers diameter [Collins et al., 2000]. The aircraft measured size distributions in general agreed quite well with the size distributions retrieved from the Mount Teide Sun photometer inversions [Formenti et al., 2000]. [23] Additional aerosol particle size and vertical distributions are available from a flight made on 8 July by a Cessna Citation in the vicinity of Tenerife [de Reus et al., 2000]. Particle size distributions in the range to 31 micrometers diameter were derived from a combination of measurements made by five different instruments, including Stockholm University s DMA and two optical particle counters. On this flight a dust layer was observed between temperature inversions at 2500 and 5500 m. Aerosol mass concentrations inside the dust layer were measured to vary between 350 to 500 mg m 3, and volume size distributions peaked at particle sizes larger than 10 micrometers in diameter. In Figure 5 we show a comparison of the modeled particle size distribution to the observations from the Pelican for 8 July at 3700 m altitude. Also plotted are the data from de Reus et al. [2000], which show the extended size distribution out to 31 mm measured at 4 km altitude (the parameters de Reus et al. [2000] fit to their data are shown in Table 1). These observations suggest that the model is not adequately accounting for the presence of particles substantially larger than about 8 mm in diameter. [24] Concurrent observations made on-board the Pelican aircraft with the NASA Ames 14-channel airborne tracking Sun photometer measured aerosol extinction between 380 and 1558 nm [Schmid et al., 2000], providing information about the vertical structure of the observed dust layers (Figure 7). For the 8 July flight a three layer structure was observed, with a polluted MBL extending to an altitude of 1000 m asl, a clean layer between 1000 and 2720 m asl, and a dust layer extending above 2720 m asl (735 hpa). On the 17 July flight a similar vertical structure was observed. For the 8 July flight the model agrees quite well with the observations in the peak altitude of extinction for the dust layer (around 3 km). As noted earlier in our discussion about lidar observations the model puts the dust layer too low for the 17 July flight. The Ångstrom exponent (449 nm/ 667 nm) measured inside the dust layer was 0.04 on both days; inside the MBL it was 0.77 on 8 July and 0.90 on 17 July. The high values measured inside the MBL indicate the presence of small particles consistent with pollutants trans- a) b) Figure 7. Comparison of the modeled optical depth and extinction vertical profiles to measurements made with the 14-channel NASA Ames Airborne Tracking Sunphotometer (AATS-14) on board the CIRPAS Pelican aircraft near Tenerife on 8 and 17 July 1997 [Schmid et al., 2000].

9 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION AAC 4-9 more positive than dust alone. Some of the temporal detail is clearly missing in the model, however, particularly at Dakar, where the model missed the passage of two shortlived dust events on 18 and 19 July. In Figure 9 we show comparisons of the modeled and retrieved particle size distributions at both sites. [26] We do not have a data set of aerosol vertical distributions at Sal and Dakar from the ACE-2 time frame, and so our validation of the aerosol vertical structure at these locations is limited. Figure 10 shows the modeled vertical dust mass concentrations at Dakar and Sal for July Also shown in Figure 10 are the vertical potential temperature and water vapor mixing ratio profiles obtained with radiosondes at Dakar and Sal for several dates from our simulation. The profiles at these two locations are quite similar, typically having a stable maritime layer in the lowest 1 2 km of the atmosphere (characterized by high water vapor mixing ratios and an increasing potential temperature with height) and a more well-mixed, nearly isentropic layer with low water vapor content extending to about 6 km. In general, high dust concentrations do not Figure 8. Comparison of the modeled aerosol optical depth to the AERONET Sun photometer measurements at Dakar and Sal. ported from Europe (see Figure 2). The lower value of the Ångstrom exponent observed in the dust layer is consistent with the larger sizes of the dust particles, and the fact that it is negative indicates a fairly monodisperse area particle size distribution with a modal radius of about 1 mm. Both the modeled particle size distribution and the de Reus et al. [2000] measurements yield an Ångstrom exponent of about 0.07 inside the dust layer Model Validation at Dakar and Sal [25] The data available to us for model validation at Dakar and Sal Island are considerably sparser than at Tenerife. Figure 8 shows a comparison of the modeled optical depth to the AERONET data at Dakar and Sal. The magnitude of the modeled AOD averages within 20% of the observations at Sal, and averages within 50% of the observations at Dakar. The Ångstrom exponent (440 nm/ 670 nm) from the Sun photometer data generally decreases with increasing optical depth (not shown), consistent with the passage of dust plumes. The lowest observed values are about 0.05 at Sal and 0.05 at Dakar, while the modeled value is about The observations are column integrated, however, and include contributions from nondust aerosols, which are likely to make the Ångstrom exponent Figure 9. Comparison of modeled column particle size distributions averaged above sea level to AERONET data at Dakar and Sal.

10 AAC 4-10 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION a) b) c) d) Figure 10. Modeled vertical mass concentrations at Dakar and Sal. Also shown are NCEP/NCAR radiosonde observations at Dakar and Sal, showing the vertical distribution of potential temperature and water vapor on the dates indicated (1200 UTC). extend above 6 km in the model, but do often reach that high. There are frequently high dust concentrations in the lowest 1 2 km of the model, and so it is difficult to determine if the model is capturing the vertical distribution of aerosols adequately. Chiapello et al. [1995] showed surface data collected at Sal during the period December 1991 to December Measured surface mass concentrations averaged about 15 mg m 3 for the month of July during this period. Our modeled surface mass concentration at Sal averages 275 mg m 3 during July 1997, which is somewhat surprising given the usual maritime origin of the surface air at this location (Figure 2). Our sensitivity tests (not shown) indicate that the modeled surface mass concentration at Sal is dominated by dust sources along the west African coast between about 20 N and 25 N. The dust from these sources does not typically get elevated above 1.5 km and is efficiently transported to Sal at low altitudes, contrary to what is expected from observations Discussion of Model Results [27] The model is generally able to reproduce the observed temporal variability and magnitude of dust AOD when compared to available measurements from AERO- NET and the airborne Sun photometer (Figures 4, 7, and 8). Since no other aerosols are treated in the transport model, some underestimate of the total AOD is expected, and we point out that the modeled sea level AOD at Tenerife is generally underestimated at times when dust is not present over the island. The AOD at higher altitudes (Izana and Mount Teide) is generally close to zero except during dust events, and so we attribute the missing AOD at sea level to sea salt and anthropogenic pollutant type aerosols not treated in the model. We will discuss the significance of these aerosols on our radiative transfer simulations in the next section. [28] The model is generally able to reproduce the observed particle size distributions which compared with observations from aircraft and AERONET (Figures 5 and 9). A significant discrepancy between the modeled and observed size distributions is only apparent when the model is compared to the de Reus et al. [2000] data (Figure 5), where the model greatly underestimates the mass of particles with diameters greater than about 10 mm. Particles with a diameter of 10 mm have a fall speed of about 1 cm s 1, corresponding to a vertical fall of about 4.5 km over 5 days. The trajectories in Figure 2 indicate that the high altitude air arriving at Tenerife on 8 July had its origins 5 days earlier over Algeria, so in order for such large particles to have originated from there they must have been initially pumped up to very high altitudes (>5 km). There are more large particles present in the model at lower altitudes than at the altitude shown in Figure 5, and so we speculate that either the particles were not initially lifted to a high enough altitude over the source or that the vertical mixing was not strong enough during transport to keep them elevated. These large particles do not contribute significantly to the AOD, but we will discuss their possible significance to the scattered radiance field in the following section. [29] In Figure 7 we showed that modeled vertical distribution of dust near Tenerife was in good agreement with the profile obtained from the CIRPAS Pelican aircraft on 8 July. By contrast, the modeled distribution was at too low of an altitude for the 17 July dust event (Figures 6 and 7). The back trajectories shown in Figure 2 indicate the dust originates in different source regions for these two events, primarily in Algeria for the 8 July event and in Libya for the

11 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION AAC 4-11 Table 2. Summaries of the Dependence of Aerosol Optical Depth and Aerosol Index on Various Dust Radiative Properties Radiative Parameter t 440 AI t 440 /AI How Constrained? Surface albedo weak weak sensitivity tests Height strong strong ACE-2 data Mass concentration strong strong weak ACE-2 data Particle size distribution strong moderate moderate ACE-2 data Refractive index (real) weak weak weak sensitivity tests Refractive index (imaginary) weak strong strong ACE-2 and Hsu et al. [1999] Data Viewing angle moderate moderate comparison to EP-TOMS overpass Particle shape??? not explored in this study 17 July event. We have conducted a number of sensitivity tests by running the transport model with various sources turned on and off. The modeled dust AOD profile at Tenerife on 8 July8 is shown in this way to consist of approximately equal amounts of dust from Algerian and Libyan sources (about 70%), and a smaller contribution from sources in Mali (20%), all transported at about 3 km altitude. The dust at Tenerife on 17 July has its greatest contribution (40%) from Libyan sources transport at about 3 km altitude (about the correct altitude), but the remainder of the optical depth is from sources in Algeria, Mali, and along the coast, all of which are transported at altitudes between about 1 and 2 km. The dust from the coastal sources is expected to remain at low altitudes because of the generally stable boundary layer in that region, and it is this dust which is transported in the surface layer to Sal causing unrealistically high surface mass concentrations modeled there. Clearly, the lifting mechanism over the continent for this event does not pump enough dust to high altitudes while the coastal sources contribute too much dust. A more satisfactory vertical profile is obtained by increasing the strength of sources in Libya, but reproducing the tight layering observed with the lidar is still beyond the capability of our model due to relatively coarse vertical resolution. We will further discuss the implications of the aerosol vertical distribution on our radiative transfer simulations in the following section. 4. Radiative Transfer Calculations and Dust Refractive Index [30] The modeled dust fields are used to simulate the backscattered UV radiances at 331 and 360 nm, from which we construct a simulated TOMS aerosol index. In Table 2 we summarize the parameters which are relevant to simulating both the AOD and the AI. We discuss the AI in more detail in the following subsection. It is most sensitive to the aerosol mass, altitude, and single scattering albedo (which in turn depends on particle size and refractive index). We have discussed the validity of our transport model at simulating aerosol mass, altitude, and particle size distribution. The significant remaining free parameter in our radiative transfer simulations is the dust complex index of refraction. Observations demonstrate a linear relationship between the AOD and the AI at Sal and Dakar [Hsu et al., 1999]. The fact that the observed relationship is linear means the ratio of AOD to AI is independent of aerosol mass (or optical depth). Therefore we restrict our concern in the following to the sensitivity of the AOD/AI ratio to particle size distribution, altitude, and refractive index. The nonsphericity of dust particles is unfortunately not considered in this paper and is the subject of ongoing research. We consider our dust particles to be homogeneous spheres TOMS Aerosol Products [31] The TOMS suite of instruments ( particularly Nimbus-7 and EP-TOMS) have been employed to detect the presence of UV absorbing aerosols over both land and ocean surfaces by measuring the reduction in the spectral contrast relative to a purely molecular atmosphere. UVabsorbing aerosols such as dust or smoke produces a positive AI with a value near unity, while nonabsorbing aerosols yield a negative AI value [Herman et al., 1997]. Torres et al. [1998] showed theoretically that the AI increases linearly with AOD (out to t aer 1), and is sensitive to the particle single scatter albedo and the aerosol altitude. Hsu et al. [1999] confirmed and quantified the relationship between the AI and AOD by comparing Nimbus-7 and EP-TOMS AI retrievals to AOD measurements from the AERONET Sun photometers. They showed how the slope of the relationship is determined by the aerosol single scattering albedo by considering regions affected by different aerosol types (e.g., smoke and dust). The effects of aerosol altitude were illustrated by considering how the altitude of aerosol transport varies with the time of year. For summer dust aerosols off the west coast of Africa (Sal, Dakar, Barbados; May August 1997) the slope of the AERONET measured 440 nm aerosol optical depth to the EP-TOMS AI relationship (AOD/AI) was between 0.33 and These retrieved slopes out over the ocean differed from those derived at inland sites, closer to the sources, where the dust is at a different altitude. Similarly, these slopes differed from those retrieved during the winter months, where the dust transport out over the ocean is at a different altitude as well. [32] Because of the variability of aerosol height and single scatter albedo during long-range transport, obtaining quantitative aerosol properties from the AI is difficult. More recent efforts have focused on inverting aerosol properties from the measured radiance fields [Torres et al., 2002a]. Such an approach relies on making an appropriate choice of the model of aerosol radiative properties relevant to a particular scene viewed by the satellite. In particular, the aerosol model must provide the aerosol type (dust or carbonaceous), altitude (generally derived from a transport model), particle size distribution, and refractive index. For dust the Torres et al. [2002a] retrievals use refractive index values from the Patterson et al. [1977] UV data. A longterm climatology of aerosol optical depth has been derived

12 AAC 4-12 COLARCO ET AL.: UV IMAGINARY INDEX OF REFRACTION Figure 11. Results of a sensitivity study of variations in the computed aerosol index to select parameters. In all cases the Torres et al. [1998] D2 dust particle size distribution has been employed (see Table 1). The aerosol is assumed to be distributed vertically with a peak at 3 km altitude. from this retrieval and validated against AERONET data [Torres et al., 2002b] Methodology of Radiative Transfer Calculations [33] Our goal is to determine an appropriate choice of dust refractive index for use in the retrievals discussed above. We do this by performing a sensitivity study of the simulated AI at Tenerife, Dakar, and Sal for variations in the refractive index. We choose to simulate the AI because of its sensitivity to UV absorbing aerosols, and because we can use the observed AOD/AI relationships to determine the best dust refractive index in a manner which is independent of dust abundance. Our approach is to constrain the dust radiative properties as much as possible with the numerical model discussed above (i.e., constraining particle size and aerosol height). [34] The radiative properties for mineral dust are determined at a number of refractive indices using a Mie scattering algorithm [Wiscombe, 1980]. Integrating these properties across the modeled particle size distribution we obtain the single scatter albedo and polarized phase function moments for each level in the specified atmospheric column. The Rayleigh scattering properties for the column are determined from the pressure profile output by the MATCH model. The Rayleigh and dust properties are combined and fed into a polarized, multistream plane-parallel radiative transfer code [Evans and Stephens, 1991] to generate the scattered radiance field at 331 and 360 nm. The correction for the spectral contrast of a purely molecular scattering atmosphere is determined from a look-up table of the Rayleigh scattered radiances at the 360 nm Rayleigh optical depth given by the modeled pressure profile. The AI is derived as in the work of Herman et al. [1997]. In our calculations we have assumed an ozone optical depth of 0.05 at 331 nm, corresponding to a typical tropical profile of about 300 DU [McPeters et al., 1996] (ozone absorption is unimportant at 360 nm). Uncertainties in the ozone optical depth and vertical profile will affect the computed radiances, but through sensitivity tests (not shown) we determined that uncertainties in the ozone abundance contribute less than 0.5% error in the simulated AI. [35] The simulated AI also depends on the surface reflectivity and the real index of refraction of the dust particles. The surface reflectivity in the UV is generally low and spatially invariant. Values are retrieved from longterm TOMS observations as described by Herman and Celarier [1997]. The real UV refractive index for typical dust aggregates is somewhere between 1.45 and 1.65 [Sokolik and Toon, 1999]. Sensitivity tests carried out over a wide range of scattering angles have shown that the computed AI values vary at most by ±5% over this range in the real refractive index as the surface reflectivity is varied between 0% and 12% (Figure 11). Also shown in Figure 11 is the effect of varying the solar zenith angle on the calculations. The effects of these variations on the simulated AI are generally small. Since the surface reflectivity and viewing geometry are part of the satellite data set, our chief uncertainty is the real refractive index, which we take for the remainder of this paper to have a value of Results of Radiative Transfer Calculations [36] We calculated the AI using model output for days on which EP-TOMS observations are available, choosing the view parameters for the EP-TOMS footprint nearest the AERONET Sun photometer location. We further restrict our simulations to instances when the EP-TOMS field-ofview is not obscured by clouds (determined by allowing retrievals only for scenes for which the spectrometer is not in its most oblique scan positions and for which the measured 360 nm reflectivity is less than 10%). A direct comparison of a time series of the modeled AI to the EP-TOMS retrievals at a particular location is complicated because of instances where the model does not predict the correct aerosol optical depth (as in Figures 4 and 8). We can eliminate the sensitivity of the calculated AI to the modeled AOD by exploiting the linearity of the relationship between AOD and AI. The ratio of AOD/AI is sensitive to aerosol particle size distribution, vertical distribution, and complex refractive index Results for a Spectrally Invariant Refractive Index [37] In Figure 12 we compare the modeled AOD to the AI computed using the satellite viewing geometry for days when EP-TOMS data were available at Tenerife. These simulations were run using three different refractive indices for dust, assuming a gray aerosol; that is, one for which the refractive index is wavelength independent. These three cases illustrate the variation in the modeled AI as we change the amount of absorption in the dust layer, with AI increasing as dust absorbtion increases as indicated by smaller slopes.