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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.

[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.

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.