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Surface remote sensing of aerosol properties provides "ground truth" for satellite and model validation, and is an important component of aerosol observation system. Due to the different characteristics of background aerosol variability, information obtained at different locations usually have different spatial representativeness, implying that the location should be carefully chosen so that its measurement could be extended to a greater area. In this study, we present an objective observation array design technique that automatically determines the optimal locations with the highest spatial representativeness based on the Ensemble Kalman Filter (EnKF) theory. The ensemble is constructed using aerosol optical depth (AOD) products from five satellite sensors. The optimal locations are solved sequentially by minimizing the total analysis error variance, which means that observations at these locations will reduce the background error variance to the largest extent. The location determined by the algorithm is further verified to have larger spatial representativeness than some other arbitrary location. In addition to the existing active AERONET sites, the 40 selected optimal locations are mostly concentrated on regions with both high AOD inhomogeneity and its spatial representativeness, namely the Sahel, South Africa, East Asia and North Pacific Islands. These places should be the focuses of establishing future AERONET sites in order to further reduce the uncertainty in the monthly mean AOD. Observations at these locations contribute to approximately 50% of the total background uncertainty reduction.

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

Spectral dependence of aerosol single-scattering albedo (SSA) has been used to infer aerosol composition. In particular, aerosol mixtures dominated by dust absorption will have monotonically increasing SSA with wavelength while that dominated by black carbon absorption has monotonically decreasing SSA spectra. However, by analyzing SSA measured at four wavelengths, 440, 675, 870, and 1020 nm from the Aerosol Robotic Network data set, we find that the SSA spectra over East Asia are frequently peaked at 675 nm. In these cases, we suggest that SSA spectral curvature, defined as the negative of the second derivative of SSA as a function of wavelength, can provide additional information on the composition of these aerosol mixtures. Aerosol SSA spectral curvatures for East Asia during fall and winter are considerably larger than those found in places primarily dominated by biomass burning or dust aerosols. SSA curvature is found to increase as the SSA magnitude decreases. The curvature increases with coarse mode fraction (CMF) to a CMF value of about 0.4, then slightly decreases or remains constant at larger CMF. Mie calculations further verify that the strongest SSA curvature occurs at approx. 40% dust fraction, with 10% scattering aerosol fraction. The nonmonotonic SSA spectral dependence is likely associated with enhanced absorption in the shortwave by dust, absorption by black carbon at longer wavelengths, and also the flattened absorption optical depth spectral dependence due to the increased particle size.

Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.

Many remote sensing techniques and passive sensors have been developed to measure global aerosol properties. While instantaneous comparisons between pixel-level data often reveal quantitative differences, here we use Empirical Orthogonal Function (EOF) analysis, also known as Principal Component Analysis, to demonstrate that satellite-derived aerosol optical depth (AOD) data sets exhibit essentially the same spatial and temporal variability and are thus suitable for large-scale studies. Analysis results show that the first four EOF modes of AOD account for the bulk of the variance and agree well across the four data sets used in this study (i.e., Aqua MODIS, Terra MODIS, MISR, and SeaWiFS). Only SeaWiFS data over land have slightly different EOF patterns. Globally, the first two EOF modes show annual cycles and are mainly related to Sahara dust in the northern hemisphere and biomass burning in the southern hemisphere, respectively. After removing the mean seasonal cycle from the data, major aerosol sources, including biomass burning in South America and dust in West Africa, are revealed in the dominant modes due to the different interannual variability of aerosol emissions. The enhancement of biomass burning associated with El Nino over Indonesia and central South America is also captured with the EOF technique.

We investigate the roles of climate forcings and chaos (unforced variability) in climate change via ensembles of climate simulations in which we add forcings one by one. The experiments suggest that most interannual climate variability in the period 1979–1996 at middle and high latitudes is chaotic. But observed SST anomalies, which themselves are partly forced and partly chaotic, account for much of the climate variability at low latitudes and a small portion of the variability at high latitudes. Both a natural radiative forcing (volcanic aerosols) and an anthropogenic forcing (ozone depletion) leave clear signatures in the simulated climate change that are identified in observations. Pinatubo aerosols warm the stratosphere and cool the surface globally, causing a tendency for regional surface cooling. Ozone depletion cools the lower stratosphere, troposphere and surface, steepening the temperature lapse rate in the troposphere. Solar irradiance effects are small, but our model is inadequate to fully explore this forcing. Well-mixed anthropogenic greenhouse gases cause a large surface wanning that, over the 17 years, approximately offsets cooling by the other three mechanisms. Thus the net calculated effect of all measured radiative forcings is approximately zero surface temperature trend and zero heat storage in the ocean for the period 1979–1996. Finally, in addition to the four measured radiative forcings, we add an initial (1979) disequilibrium forcing of +0.65 W/m2. This forcing yields a global surface warming of about 0.2°C over 1979–1996, close to observations, and measurable heat storage in the ocean. We argue that the results represent evidence of a planetary radiative imbalance of at least 0.5° W/m2; this disequilibrium presumably represents unrealized wanning due to changes of atmospheric composition prior to 1979. One implication of the disequilibrium forcing is an expectation of new record global temperatures in the next few years. The best opportunity for observational confirmation of the disequilibrium is measurement of ocean temperatures adequate to define heat storage.

The Twomey effect is an increase of the cloud albedo with increasing concentration of tropospheric aerosols serving as cloud condensation nuclei (CCN). Confirmation and quantification of this effect on a global basis requires accurate satellite retrievals of CCN concentrations. We present a theoretical study of the ability of passive satellite remote sensing techniques to provide reliable estimates of tropospheric aerosol column densities over the ocean. We show that a retrieval algorithm based on single-channel single-viewing-angle radiance measurements is incapable of accurately determining CCN column densities and that an algorithm based on multiangle radiance measurements provides much better retrievals. However, even for the latter algorithm the errors in the retrieved CCN column densities can exceed a factor of 5. The poor performance of single-channel radiance-only algorithms is explained by the strong dependence of the extinction cross section and weak dependence of the phase function on aerosol effective radius. In contrast, high-precision multiangle polarization measurements, which are much more sensitive to aerosol microphysics, are capable of constraining CCN column densities to within a few tens of percent.

Zenith-enhanced backscattering (ZEB) of a lidar beam by cirrus clouds is a remarkable phenomenon usually explained in terms of specular reflection from large plane facets of horizontally oriented ice plates. Since the standard geometric optics approximation (GO) may be inapplicable in many cases, especially in analyzing infrared measurements, and ignores physical optics effects, we use the recently improved exact T-matrix method to compute the scattering of light by ice plates at visible and infrared wavelengths. Computations for horizontally and randomly oriented thin disks and oblate spheroids with size parameters up to 50 show that while all particles produce a strong Fraunhofer diffraction peak centered at exactly the forward-scattering direction, a strong and narrow ZEB peak can be produced only by horizontally oriented disks but not by horizontally oriented spheroids or particles in random orientation. This finding demonstrates that ZEB can be produced even by particles which are not in the GO domain of size parameters and supports the traditional interpretation of ZEB. Also, we have found that the angular width of the ZEB peak for horizontally oriented disks is equal to half the width of the Fraunhofer diffraction peak. This result can be used in practice to derive a lower estimate of ice particle sizes from high angular resolution measurements of ZEB. We show that our exact T-matrix computations can explain the peculiar zenith-angle dependence of depolarization observed by Platt et al. [1978] in the visible and can be interpreted qualitatively in terms of the modified Kirchhoff approximation.

T-matrix computations of light scattering by polydispersions of randomly oriented nonspherical aerosols and Mie computations for equivalent spheres are compared. Findings show that even moderate nonsphericity results in suubstantial errors in the retrieved aerosol optical thickness if satellite reflectance measurements are analyzed using Mie theory. On the other hand, the use of Mie theory for nonspherical aerosols produces negligible errors in the computation of albedo and flux related quantities, provided that the aerosol size distribution and optical thickness are known beforehand. The first result can be explained by large nonspherical-spherical differences in scattering phase function, while the second result follows from small nonspherical-spherical differences in single-scattering albedo and asymmetry parameter. No cancellation of errors occurs if one consistently uses Mie theory in the retrieval algorithm and then in computing the albedo for the retrieved aerosol optical thickness.

El Nino.Southern Oscillation (ENSO) is the dominant mode of interannual variability in the tropical atmosphere. ENSO could potentially impact local and global aerosol properties through atmospheric circulation anomalies and teleconnections. By analyzing aerosol properties, including aerosol optical depth (AOD) and Angstrom exponent (AE; often used as a qualitative indicator of aerosol particle size) from the Moderate Resolution Imaging Spectrometer, the Multiangle Imaging Spectroradiometer and the Sea ]viewing Wide Field ]of ]view Sensor for the period 2000.2011, we find a strong correlation between the AE data and the multivariate ENSO index (MEI) over the tropical Pacific. Over the western tropical Pacific (WTP), AE increases during El Nino events and decreases during La Nina events, while the opposite is true over the eastern tropical Pacific (ETP). The difference between AE anomalies in the WTP and ETP has a higher correlation coefficient (>0.7) with the MEI than the individual time series and could be considered another type of ENSO index. As no significant ENSO correlation is found in AOD over the same region, the change in AE (and hence aerosol size) is likely to be associated with aerosol composition changes due to anomalous meteorological conditions induced by the ENSO. Several physical parameters or mechanisms that might be responsible for the correlation are discussed. Preliminary analysis indicates surface wind anomaly might be the major contributor, as it reduces sea ]salt production and aerosol transport during El Nino events. Precipitation and cloud fraction are also found to be correlated with tropical Pacific AE. Possible mechanisms, including wet removal and cloud shielding effects, are considered. Variations in relative humidity, tropospheric ozone concentration, and ocean color during El Nino have been ruled out. Further investigation is needed to fully understand this AE ]ENSO covariability and the underlying physical processes responsible for it.

Voyager IRIS observations of the NEB reveal longitudinal variability of 5-micron brightness temperatures of order 100 C. An anisotropic multiple scattering radiative transfer model is used to calculate synthetic spectra for comparison with the IRIS observations. Mie theory is used to model the spectral dependence of cloud extinction from 180 to 2300/cm. Cloud base locations within the model vary with assumed gas abundances according to thermochemical equilibrium. It is found that spatial variations in the abundance profiles of the condensible species, parahydrogen profiles and cloud optical depths can be used as tracers of the local and large-scale dynamics. The variation of cloud opacity is strongly correlated with the variation of relative humidity, which suggests that dynamic depletion of water vapor above the cloud forming level is the most plausible model to explain the spatial variation in the water profile within the NEB.

[1] Absorbing aerosols, especially mineral dust and black carbon, play key roles in climate change by absorbing solar radiation, heating the atmosphere, and contributing to global warming. In this paper, we first examine the consistency of the Aerosol Index (AI) product as measured by the Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) instruments and then analyze these AI data sets to investigate the temporal and spatial variability of UV absorbing aerosols. In contrast to the trend in aerosol optical depth found in the advanced very high-resolution radiometer data, no obvious long-term trend in absorbing aerosols is observed from the time series of AI records. The comparison between the mean annual cycle in the two data sets shows that the cycles agree very well both globally and regionally, indicating a consistency between the AI products from TOMS and OMI. Varimax rotated Empirical Orthogonal Function (EOF) analysis of detrended, deseasonalized AI data proves to be successful in isolating major dust and biomass burning source regions, as well as dust transport. Finally, we find that large, individual events, such as the Kuwait oil fire and Australian smoke plum, are isolated in individual higher-order principal components.

[1] Measurements by multifilter rotating shadowband radiometers (MFRSRs) constitute a valuable global data set with contributions from hundreds of instruments deployed worldwide. The geographical coverage of MFRSR networks is complementary to that of AERONET and often provides better spatial density of measurement sites, especially in the United States. We describe our recently updated analysis algorithm for MFRSR data that allows partitioning of the spectral aerosol optical depth (AOD) into fine and coarse mode AOD and retrieval of the fine mode effective radius. Our recent sensitivity study demonstrated that for a typical measurement accuracy 0.01 of AOD, the trade-offs between the spectral aerosol extinction and NO2 absorption in the visible range effectively prevent unambiguous retrieval of NO2 column from MFRSR data and may also bias aerosol size distribution retrievals. This has prompted us to adopt a new retrieval approach, which utilizes climatological NO2 (based on SCIAMACHY satellite retrievals) and uses column ozone from TOMS measurements. The performance of this new approach was evaluated using the long-term data set from the Southern Great Plains (SGP) site operated by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program. We present a detailed intercomparison of total, fine, and coarse mode AOD and fine mode effective radius between two MFRSRs located at the SGP's Central Facility and with the correlative AERONET Sun-sky inversion results (Version 2) derived from a collocated CIMEL Sun photometer. The comparison between two MFRSRs demonstrated good consistency of both the measurements and the analysis. Agreement with AERONET inversions is remarkably good, in that differences in AOD components do not exceed the expected measurement accuracy of 0.01, while the retrieved values of fine mode effective radius show no relative bias and only 0.03 μm random error (standard deviation of the differences). We show that if only data with large enough AOD (more than 0.06 at 870 nm) are selected, this error is reduced by a factor of two, becoming about 10% of a typical fine mode effective radius value.

[1] Empirical Orthogonal Function (EOF) analysis is performed on ground-based shadowband radiometer and Sun photometer data. The data come from Multifilter Rotating Shadowband Radiometer (MFRSR) instruments located at the central and extended facilities of the Southern Great Plains (SGP) research site of the Department of Energy's Atmospheric Radiation Measurement (ARM) program, as well as the Rotating Shadowband Spectroradiometer (RSS) 102 and 105 and the CIMEL Sun photometer CSPHOT located at the central facility at SGP. The EOFs show the variability in total aerosol optical depth and provide some qualitative information on the separation of the coarse and fine aerosol modes. In addition, as has been demonstrated previously with satellite data, EOF analysis also exposes several flaws and inconsistencies within the ground-based data sets. These inhomogeneities include optical depth anomalies in some MFRSR filters, wavelength shifts in the RSS, and problems with the data transmitter of the CIMEL instrument. Therefore EOF analysis is shown to be a quick and effective means not only of assessing the general aerosol behavior in the air above a particular monitoring instrument, but also of identifying both known and unanticipated influences on the data coming from within the instrument itself.

[1] A physically based aerosol climatology is important to address questions of global climate change. We evaluate the aerosol climatology used in the GISS ModelE (Schmidt et al., 2006), by characterizing and comparing the geographic distribution and seasonal variability of aerosol optical depth (AOD) and particle size via Angstrom exponent (A) against available satellite and ground-based measurements, i.e., MODIS, MISR, POLDER, AVHRR, and AERONET data. There are a number of model parameters, particularly those related to aerosol size specification, that can be better constrained by comparison to satellite data. Our comparison shows that there are large differences in the satellite and ground-based global distributions of AOD. The differences between the observations increase for the Angstrom exponent. Given the uncertainties associated with satellite retrieval results, the agreement in the distributions of global optical depth between GCM aerosols and satellite data is qualitatively reasonable. However, the Angstrom exponent of the GCM aerosol is clearly biased low compared to satellite data, implying that the GCM aerosol sizes are overestimated. There is qualitative agreement of the ModelE aerosol single scattering albedo ϖ with TOMS Aerosol Index (AI) and AERONET data. The comparisons show insufficient aerosol absorption at most locations, suggesting a possible underestimation of black carbon distributions in the GCM. However, a more quantitative comparison first requires a readjustment of the GCM aerosol size specification.

[1] A new MFRSR data analysis algorithm is presented. Our earlier algorithm assumed a monomodal aerosol size distribution, while the new algorithm allows us to partition the aerosol optical thickness into fine and coarse aerosol modes. In addition, we retrieve the fine mode effective radius and Angstrom exponent. A bimodal gamma distribution is used to describe the aerosol particle size distribution. The algorithm has been tested using a multi-year data set from the local MFRSR network at the DOE Atmospheric Radiation Measurement (ARM) Program site in Southern Great Plains (SGP). Our retrieved aerosol optical thicknesses (total, fine, and coarse) are compared with the corresponding AERONET almucantar retrieval results derived from a CIMEL sunphotometer co-located with the MFRSR at the SGP Central Facility. A constrained variant of the algorithm (zero NO2 column values) has been used to define the range of physically justified values of the fine mode effective radius, and for comparison with AERONET particle size retrievals. We use the multiple MFRSR measurements obtained during the year 2000 at the SGP Extended Facilities to examine geographical and seasonal variability of aerosol properties. A correspondence has been found between the geographical variation in the fine mode particle size and aerosol composition (nitrates versus sulfates) as measured by National Atmospheric Deposition Program. We similarly find good correspondence between our retrieved aerosol sizes and the PM2.5 to PM10 ratios obtained from EPA AirData monitoring. Examination of the data from the SGP Central Facility obtained for the period 1993–1997 reveals a decreasing trend in coarse mode aerosol optical thickness during the 1993–1995 period, consistent with the decay of the stratospheric aerosol following the 1991 eruption of Mt. Pinatubo. In contrast, fine mode optical thickness exhibits only seasonal variability with summer maxima during this period.

[1] The rotating shadowband spectroradiometer (RSS) is a high-resolution device that measures the total, direct, and diffuse intensity of sunlight at 1016 different wavelengths. RSS data can be used to retrieve gas amounts and aerosol properties as well as to assess the accuracy of retrievals using data from lower-spectral resolution instruments, such as the multifilter rotating shadowband radiometer (MFRSR). An algorithm to retrieve aerosol and gas amounts and the aerosol size distribution from RSS data, using the full resolution and using selected wavelengths, has been developed (Gianelli, 2004). The results of the retrievals, applied to RSS data from the southern Great Plains (SGP) site, indicate a number of things about the aerosol size distribution and our ability to retrieve aerosol information accurately. First, we show that the aerosol size distribution at SGP is at least bimodal. The notion that a more complex size distribution can be modeled with an appropriately selected monomodal distribution produces an unacceptable fit to the data once the separation of aerosol extinction from nitrogen dioxide absorption at short wavelengths is taken into consideration. Second, we find that the amount of retrievable aerosol information is limited by the wavelength range of the data. This is exemplified by the indeterminacy of the coarse-mode effective radius and the interdependence in the retrievals of the fine-mode effective radius and effective variance. Third, when the fine-mode effective variance is constrained, an annual cycle in fine-mode effective radius values emerges from the retrieval results, with a maximum in March and a minimum in September. Conceivably, changes in the effective variance could influence the observed pattern as well. The large margin of error in the coarse-mode effective radius leads to relatively small error bars for the coarse- and fine-mode optical depths, the fine-mode effective radius, and ozone, except on those days when the coarse-mode optical depth is high. Examining the retrieval results for different wavelength combinations of five RSS channels allows us to investigate whether or not the MFRSR channels are optimized to retrieve aerosol information or if a different filter set would increase the robustness of the retrievals. We show that replacing the 670 nm channel with one at either 375 or 1034 nm improves the retrieval accuracy. In particular, retrievals with the 1034 nm channel very closely reproduce the full RSS retrieval results, provided that the NO2 column amount can be determined by other means. The precision of the retrieved values of the fine-mode effective radius is shown to be strongly sensitive to the precision of measured NO2 amounts; a margin of error in column NO2 of 0.3 Dobson units results in a corresponding margin of error of 0.04 μm in the fine-mode effective radius. This confirms that aerosol sizes cannot be inferred accurately from optical depth spectra alone if either the amount of NO2 above a site is unknown or an inaccurate value is assumed.

Accurate retrievals of aerosol size distribution are necessary to estimate aerosols' impact on climate and human health. The inversions of the Aerosol Robotic Network (AERONET) usually retrieve bimodal distributions. However, when the inversion is applied to Saharan and Sahelian dust, an additional mode of intermediate size between the coarse and fine modes is sometimes seen. This mode explains peculiarities in the behavior of the Angstrom exponent, along with the fine mode fraction retrieved using the spectral deconvolution algorithm, observed in a March 2006 dust storm. For this study, 15 AERONET sites in northern Africa and on the Atlantic are examined to determine the frequency and properties of the intermediate mode. The mode is observed most frequently at Ilorin in Nigeria. It is also observed at Capo Verde and multiple sites located within the Sahel but much less frequently at sites in the northern Sahara and the Canary Islands. The presence of the intermediate mode coincides with increases in Angstrom exponent, fine mode fraction, single-scattering albedo, and to a lesser extent percent sphericity. The Angstrom exponent decreases with increasing optical depth at most sites when the intermediate mode is present, but the fine mode fraction does not. Single-scattering albedo does not steadily decrease with fine mode fraction when the intermediate mode is present, as it does in typical mixtures of dust and biomass-burning aerosols. Continued investigation is needed to further define the intermediate mode's properties, determine why it differs from most Saharan dust, and identify its climate and health effects.

[1] A new automated cloud screening algorithm for ground-based sun-photometric measurements is described and illustrated on examples of real (MFRSR) and simulated data. The algorithm uses single channel direct beam measurements and is based on variability analysis of retrieved optical thickness. To quantify this variability the inhomogeneity parameter e is used. This parameter is commonly used for cloud remote sensing and modeling, but not for cloud screening. In addition to this an adjustable enveloping technique is applied to control strictness of the selection method. The key advantages of this technique are its objectivity, computational efficiency and the ability to detect short clear-sky intervals under broken cloud cover conditions. Moreover, it does not require any knowledge of the instrument calibration. The performance of the method has been compared with that of AERONET cloud screening algorithm.

Voyager Infrared Interferometer Spectrometer (IRIS) observations of Jupiter in the far infrared (180-1200/cm) are sensitive to emission originating from pressures less than 2 bars, while the 5-micrometer (1800-2300/cm) observations are primarily sensitive to emission originating from pressures greater than 2 bars. We use these differences in the location of the peak emission level to constrain the properties of the upper tropospheric cloud structure from the far-infrared observations and then use the 5-micrometer observations, with the upper tropospheric cloud structure fixed, to constrain the deep cloud structure. The relationship between observed 45- and 5-micrometer brightness temperatures reveals three distinct regions between +/- 25 deg latitude: North Equatorial Belt hot spots, which are 'hot' at both 45 and 5 micrometers; Equatorial Zone spectra, which are 'warm' at 45 micrometers and 'cold' at 5 micrometers; and North Tropical Zone spectra, which are 'cold' at both 45 and 5 micrometers. We find that the hot extreme spectral ensemble is unique to belts, and the cold extreme spectral ensembles are unique to zones, but that all other intermediate spectral ensembles are common to both regions. Analyses of these spectra using an anisotropic multiple scattering radiative transfer model reveal that the primary difference between belts and zones is the increased opacity and vertical extent of clouds in zones relative to their belt counterparts. In addition, we find a shift in the location of the para hydrogen gradient toward lower pressures in zones, with an increase in the 'equilibrated' cloud-top para fraction. We suggest that all of the variations in gas abundance profiles, temperature, and cloud structure are consistent with the effects of a simple mean circulation and large-scale wave motions.