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International audience; Tropospheric anthropogenic aerosols contribute the second-largest forcing to climate change, but with high uncertainty owing to their spatio-temporal variability and complicated optical properties. In this Review, we synthesize understanding of aerosol observations and their radiative and climate effects. Aerosols offset about one-third of the warming effect by anthropogenic greenhouse gases. Yet, in regions and seasons where the absorbing aerosol fraction is high — such as South America and East and South Asia — substantial atmospheric warming can occur. The internal mixing and the vertical distribution of aerosols, which alters both the direct effect and aerosol-cloud interactions, might further enhance this warming. Despite extensive research in aerosol-cloud interactions, there is still at least a 50% spread in total aerosol forcing estimates. This ongoing uncertainty is linked, in part, to the poor measurement of anthropogenic and natural aerosol absorption, as well as the little-understood effects of aerosols on clouds. Next-generation, space-borne, multi-angle polarization and active remote sensing, combined with in situ observations, offer opportunities to better constrain aerosol scattering, absorption and size distribution, thus, improving models to refine estimates of aerosol forcing and climate effects.

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 ~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.

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.

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.

International audience; Satellite- and ground-based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474-member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground-based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root-mean-square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root-mean-square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space-time variability.

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.

Because of the importance of clouds in modulating Earth’s energy budget, it is critical to understand their variability in space and time for climate and modeling studies. This study examines the consistency of the spatiotemporal variability of cloud amount (CA) and cloud-top pressure (CTP) represented by five 7-yr satellite datasets from the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project, and total cloud fraction observation from the Extended Edited Cloud Reports Archive (EECRA). Two spectral analysis techniques, namely combined maximum covariance analysis (CMCA) and combined principal component analysis (CPCA), are used to extract the dominant modes of variability from the combined datasets, and the resulting spatial patterns are compared in parallel. The results indicate that the datasets achieve overall excellent agreement on both seasonal and interannual scales of variability, with the correlations between the spatial patterns mostly above 0.6 and often above 0.8. For seasonal variability, the largest differences are found in the Northern Hemisphere high latitudes and near the South African coast for CA and in the Sahel region for CTP, where some differences in the phase and strength of the seasonal cycle are found. On interannual scales, global cloud variability is mostly associated with major climate modes, including El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IODM), and the datasets also agree reasonably well. The good agreement across the datasets supports the conclusion that they are describing cloud variations with these climate modes.

The Aerosol Robotic Network (AERONET) has been providing high-quality retrievals of aerosol optical properties from the surface at worldwide locations for more than a decade. Many sites have continuous and consistent records for more than 10 years, which enables the investigation of long-term trends in aerosol properties at these locations. In this study, we present the results of a trend analysis at selected stations with long data records. In addition to commonly studied parameters such as aerosol optical depth (AOD) and Ångström exponent (AE), we also focus on inversion products including absorption aerosol optical depth (ABS), single-scattering albedo (SSA) and the absorption Ångström exponent (AAE). Level 2.0 quality assured data are the primary source. However, due to the scarcity of level 2.0 inversion products resulting from the strict AOD quality control threshold, we have also analyzed level 1.5 data, with some quality control screening to provide a reference for global results. Two statistical methods are used to detect and estimate the trend: the Mann–Kendall test associated with Sen's slope and linear least-squares fitting. The results of these statistical tests agree well in terms of the significance of the trend for the majority of the cases. The results indicate that Europe and North America experienced a uniform decrease in AOD, while significant (>90%) increases in these two parameters are found for North India and the Arabian Peninsula. The AE trends turn out to be different for North America and Europe, with increases for the former and decreases for the latter, suggesting opposite changes in fine/coarse-mode fraction. For level 2.0 inversion parameters, Beijing and Kanpur both experienced an increase in SSA. Beijing also shows a reduction in ABS, while the SSA increase for Kanpur is mainly due the increase in scattering aerosols. Increased absorption and reduced SSA are found at Solar_Village. At level 1.5, most European and North American sites also show positive SSA and negative ABS trends, although the data are more uncertain. The AAE trends are less spatially coherent due to large uncertainties, except for a robust increase at three sites in West Africa, which suggests a possible reduction in black carbon. Overall, the trends do not exhibit obvious seasonality for the majority of parameters and stations.

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.

The nominal Global Aerosol Climatology Project (GACP) record of aerosol optical thickness (AOT) and Angstrom exponent (AE) over the oceans is extended by 6 months to cover the period from August 1981 through December 2005. The most recent 4-year segment reveals no significant short-term tendencies in globally and hemispherically averaged AOTs and AEs. This finding is consistent with contemporaneous MODIS and MISR results and the accumulating evidence of a gradual transition from global brightening to global dimming. We also analyze the retrieval implications of allowing the imaginary part of the aerosols refractive index Im( m ) to change over the duration of the GACP record. Our sensitivity study shows that increasing Im( m ) from 0.003 during the 4-year pre-Pinatubo period up to 0.007 during the most recent 4-year segment of GACP data eliminates the previously identified long-term decreasing AOT trend. Should this long-term trend in Im( m ) be real then it would cause the global absorption AOT over the oceans to more than double and the global single-scattering albedo to decrease from ∼0.95 to ∼0.88. Such changes could make tropospheric aerosols significant contributors to the recent surface temperature increase.

The dominant interannual El Niño–Southern Oscillation (ENSO) phenomenon and the short length of climate observation records make it difficult to study long-term climate variations in the spatiotemporal domain. Based on the fact that the ENSO signal spreads to remote regions and induces delayed climate variation through atmospheric teleconnections, an ENSO-removal method is developed through which the ENSO signal can be approximately removed at the grid box level from the spatiotemporal field of a climate parameter. After this signal is removed, long-term climate variations are isolated at mid- and low latitudes in the climate parameter fields from observed and reanalysis datasets. This paper addresses the long-term global warming trend (GW); a companion paper concentrates on Pacific pan-decadal variability (PDV). The warming that occurs in the Pacific basin (approximately 0.4 K in the twentieth century) is much weaker than in surrounding regions and the other two ocean basins (approximately 0.8 K). The modest warming in the Pacific basin is likely due to its dynamic nature on the interannual and decadal time scales and/or the leakage of upper ocean water through the Indonesian Throughflow. Based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), a comprehensive atmospheric structure associated with the GW trend is given. Significant discrepancies exist between the two datasets, especially in the tightly coupled dynamics and water vapor fields. The dynamics fields based on NCEP–NCAR, which show a change in the Walker Circulation, are consistent with the GW change in the surface temperature field. However, intensification in the Hadley Circulation is associated with GW trend in ERA-40 instead.

The spatiotemporal structure of Pacific pan-decadal variability (PDV) is isolated in global long-term surface temperature (ST) datasets and reanalysis atmospheric parameter fields from which El Niño–Southern Oscillation (ENSO) effects have been removed. Empirical orthogonal function (EOF) and combined EOF analysis of the resulting time series identify PDV as one of two primary modes of long-term variability, the other being a global warming (GW) trend, which is addressed in a companion paper (Part I). In this study, it is shown that one of several PDV interdecadal regime shifts occurred during the 1990s. This significant change in the Pacific basin is comparable but antiphase to the well-known 1976 climate regime shift and is consistent with the observed changes in biosystems and ocean circulation. A comprehensive picture of PDV as manifested in the troposphere and at the surface is described. In general, the PDV spatial patterns in different parameter fields share some similarities with the patterns associated with ENSO, but important differences exist. First, the PDV circulation pattern is shifted westward by about 20° and is less zonally extended than that for ENSO. The westward shift of the PDV wave train produces a different North American teleconnection pattern that is more west–east oriented. The lack of a strong PDV surface temperature (ST) signal in the west equatorial Pacific and the relatively strong ST signal in the subtropical regions are consistent with an atmospheric overturning circulation response that differs from the one associated with ENSO. The analysis also suggests that PDV is a combination of decadal and/or interdecadal oscillations interacting through teleconnections.

A number of passive satellite instruments have been used to develop global climatologies of terrestrial tropospheric aerosols by analyzing the properties of sunlight reflected by the atmosphere–surface system. The outcome of these efforts are several climatologies which all purport to represent the same aerosol characteristics such as optical thickness and size. However, the quantitative differences between these climatologies have been found to far exceed the corresponding individual uncertainty claims. The magnitude of these differences is alarming and necessitates a detailed critical assessment and integrated analysis that would go far beyond simple intercomparisons of various satellite products and comparisons of satellite aerosol optical thickness results with ground-based sun-photometer data. This paper outlines the framework for a global long-term satellite climatology of aerosol properties based on a consistent combination of previous, current, and near-future satellite retrievals. We also discuss potential future strategies for deriving a much improved aerosol climatology from Earth-orbiting satellites.

In this study, we model single-scattering properties of small cirrus crystals using mixtures of polydisperse, randomly oriented spheroids and cylinders with varying aspect ratios and with a refractive index representative of water ice at a wavelength of 1.88 μm. The Stokes scattering matrix elements averaged over wide shape distributions of spheroids and cylinders are compared with those computed for polydisperse surface-equivalent spheres. The shape-averaged phase function for a mixture of oblate and prolate spheroids is smooth, featureless, and nearly flat at side-scattering angles and closely resembles those typically measured for cirrus. Compared with the ensemble-averaged phase function for spheroids, that for a shape distribution of cylinders shows a relatively deeper minimum at side-scattering angles. This may indicate that light scattering from realistic cirrus crystals can be better represented by a shape mixture of ice spheroids. Interestingly, the single-scattering properties of shape-averaged oblate and prolate cylinders are very similar to those of compact cylinders with a diameter-to-length ratio of unity. The differences in the optical cross sections, single-scattering albedo, and asymmetry parameter between the spherical and the nonspherical particles studied appear to be relatively small. This may suggest that for a given optical thickness, the influence of particle shape on the radiative forcing caused by a cloud composed of small ice crystals can be negligible.

Consistent and validated data sets of satellite-borne radiances and of a large variety of products describing the characteristics of terrestrial cloud and radiation fields have been produced within the International Satellite Cloud Climatology Project (ISCCP) covering the years 1983 through to 2003. A subset (annual and seasonal averages of the 5 year period 1991 to 1995) is used in this paper to discuss in greater detail the effect of clouds on the radiation fields at the upper and lower boundary of the atmosphere and in particular on the loss and gain (vertical divergence) of radiant energy by the atmosphere itself. Although this subset covers the effects of the Pinatubo eruption (June 1991) and of the strong El Nino event in 1992–93, which indeed caused ‘anomalies in the average aerosol and cloud fields in the tropics and subtropics’. However, our regional averages of the radiation budget at the top of the atmosphere and at ground over a period of 5 years should be within 2–5 W m−2 of longer term averages. We find very interesting spatial patterns in the global distributions of all quantities, which can be explained in part by various cloud field characteristics and by the continental surface characteristics. Most are known from similar studies with radiation budget measurements. Possibly for the first time, we show global fields of the vertical flux divergence of solar and terrestrial radiation within the atmosphere and of the effects of clouds. Both polar regions, various portions of China and the areas of persistent subtropical maritime stratocumulus fields over the Pacific and Atlantic Oceans and of cloud fields associated with the intertropical convergence zone (ITCZ) offer specific features for further analyses. This ISCCP data set seems to underestimate the absorption of solar radiation in the tropical and subtropical atmosphere by about 10 to 20 W m−2. There is a disagreement of about 30 W m−2 in global averages of the gain and loss of solar and terrestrial radiation in the atmosphere between this and two other independent data sets, which needs thorough investigation, since such data are required to validate the radiation budgets within circulation and climate models and for other climate studies. Such an assessment of radiation budget data is now under way within the auspices of the World Climate Research Programme. Copyright © 2005 Royal Meteorological Society

Statistical scale-by-scale analysis, for the first time, has been applied to the aerosol optical thickness (AOT) retrieved from the Multi-Filter Rotating Shadowband Radiometer (MFRSR) network. The MFRSR data were collected in September 2000 from the dense local network operated by the U.S. Department of Energy Atmospheric Radiation Measurement program, located in Oklahoma and Kansas. These data have 20-s temporal resolution. The instrument sites form an irregular grid with the mean distance between neighboring sites about 80 km. It is found that temporal variability of AOT can be separated into two well-established scale-invariant regimes: 1) microscale (0.5‐15 km), where fluctuations are governed by 3D turbulence, and 2) intermediate scale (15‐100 km), characterized by a transition toward large-scale 2D turbulence. The spatial scaling of AOT was determined by the comparison of retrievals between different instrument sites (distance range 30‐400 km). The authors investigate how simultaneous determination of AOT scaling in space and time can provide means to examine the validity of Taylor’s frozen turbulence hypothesis. The temporal evolution of AOT scaling exponents during the month appeared to be well correlated with changes in aerosol vertical distribution, while their spatial variability reflects the concavity/convexity of the site topography. Explanations based on dynamical processes in atmospheric convective boundary layer are suggested.

In this paper, tradeoffs between speed and accuracy for the atmospheric correction of hyperspectral imagery are examined. Among the issues addressed are the use of scattering calculations on a sparse spectral grid and consequent accuracy and speed tradeoffs, methods for minimizing the required number of quadrature points in multiple-scattering calculations, effects of the vertical profiles of aerosols and absorbing gases on atmospheric correction, and efficient approaches for including the effects of sensor variability (or imperfections) on atmospheric correction.

A retrieval algorithm for processing multifilter rotating shadowband radiometer (MFRSR) data from clear and partially cloudy days is described and validated. This method, while complementary to the Langley approach, uses consistency between the direct normal and diffuse horizontal measurements combined with a regression technique to simultaneously retrieve daily time series of column mean aerosol particle size, aerosol optical depth, NO2, and ozone amounts along with the instrument's calibration constants. Comparison with the traditional Langley calibration method demonstrates two advantages of the approach described here: greater calibration stability and a decreased sensitivity of retrievals to calibration errors.

Measurements from ground-based sun photometer networks can be used both to provide ground-truth validation of satellite aerosol retrievals and to produce a land-based aerosol climatology that is complementary to satellite retrievals that are currently performed mostly over ocean. The multifilter rotating shadowband radiometer (MFRSR) has become a popular network instrument in recent years. Several networks operate about a hundred instruments providing good geographical coverage of the United States. In addition, international use of the MFRSR has continued to increase, allowing MFRSR measurements to significantly contribute to aerosol climatologies. This study investigates the feasibility of creating a ground-based aerosol climatology using MFRSR measurements. Additionally, this analysis allows for testing of the performance of the retrieval algorithm under a variety of conditions. The retrieval algorithm is used for processing MFRSR data from clear and partially cloudy days to simultaneously ret...

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.

We report, for the f irst time to our knowledge, comparisons of light-scattering computations for large, randomly oriented, moderately absorbing spheroids based on the geometric-optics approximation and the exact T-matrix method. We show that in most cases the geometric-optics approximation is (much) more accurate for spheroids than for surface-equivalent spheres and can be used in phase function computations (but not in polarization computations) for nonspherical particles with size parameters as small as 60. Differences in the single-scattering albedo between geometric-optics and T-matrix results are surprisingly small, even for small size parameters.

[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.

We present further development of our analysis algorithm for Multi-Filter Rotating Shadowband Radiometer (MFRSR) data. The new additions include techniques allowing us to retrieve spectral aerosol single scattering albedo (SSA) and column amount of precipitable water vapor (PWV). The SSA retrievals employ MFRSR measurements of both direct normal and diffuse horizontal irradiances. We present a sensitivity study indicating dependence of SSA retrievals on optical thickness and other aerosol parameters. Influence on the retrievals of a possible error in separation of the direct and diffuse irradiances by the instrument is discussed. The algorithm has been tested on a long-term dataset from the local MFRSR network at the DOE Atmospheric Radiation Measurement (ARM) Program site in Southern Great Plains (SGP). Our results are compared to AERONET's almucantar retrievals of SSA from CIMEL sun-photometer co-located with the MFRSR at the SGP Central Facility. A constrained variant of the algorithm (assuming zero nitrogen dioxide column values) is used for this comparison and to study the influence of the uncertainty associated with this atmospheric gas on the retrieved aerosol absorption properties. Precipitable water vapor column amounts are determined from the direct normal irradiances in the 940 nm MFRSR spectral channel. HITRAN 2004 spectral database has been used to model the water vapor absorption, while a range of other databases (HITRAN 1996, 2000, ESA) is used in the sensitivity study. The results of the PWV retrievals for SGP's MFRSR network are compared with correlative measurements by Microwave Radiometers (MWR), GPS stations, AERONET, and MODIS satellite product. In the latter case an interpolation technique has been used to determine spatial structure of water vapor field from the network data and to create a 2D dataset comparable with satellite data.

We recently developed a new version of our analysis algorithm for Multi-Filter Rotating Shadowband Radiometer (MFRSR) data that allows us to retrieve fine mode and coarse mode aerosol optical thickness as well as the effective radius of the fine mode. Our retrieval products also include time series of column amounts of ozone and nitrogen dioxide. The algorithm has been tested using a multi-year dataset from the local MFRSR network at the DOE Atmospheric Radiation Measurement (ARM) Program site in Southern Great Plains (SGP). Here we present an overview of the retrieval algorithm and validate its performance through comparison of our MFRSR retrieval products 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 retrieval algorithm (assuming zero nitrogen dioxide column values) is described in detail. We use this variant of the algorithm to derive the range of physically justified values of the fine mode effective radius and for comparison with AERONET particle size retrievals. A multi-instrument, multi-year, MFRSR dataset from the SGP Extended Facilities is used to examine geographical and seasonal variability of aerosol properties. We find a correspondence between the geographical variation in fine mode particle size and aerosol composition (nitrates vs. sulfates) as measured by National Atmospheric Deposition Program. A similar correspondence in terms of temporal variability exists between our retrievals and in-situ measurements of aerosol composition made by NOAA Pacific Marine Environmental Laboratory (PMEL) at SGP Central Facility.

[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.

A substantial upgrade of our previously developed MFRSR data analysis algorithm is presented. The new version of the algorithm features an automated cloud screening procedure based on optical thickness variability analysis. This technique is objective, computationally efficient and is able to detect short clear-sky intervals under broken cloud conditions. The performance of the method has been compared with that of AERONET cloud screening algorithm. Another new feature is the adoption of a bimodal gamma distribution as the aerosol particle size model. The size of the fine mode particles and a ratio between optical thicknesses of the two modes are retrievable. The algorithm has been tested on a multi-year dataset from the MFRSR network at the DOE Atmospheric Radiation Measurement (ARM) Program site in Southern Great Plains (SGP). The aerosol optical thicknesses (total, fine, and coarse) obtained from our analysis were successfully compared with the corresponding AERONET almucantar retrievals from a CIMEL sunphotometer colocated with the MFRSR at the SGP Central Facility. Geographical and seasonal variability of aerosol properties has been observed in the multi-instrument dataset from all SGP Extended Facilities for the year 2000. The geographical trends in the fine mode particle size appear to reflect differences in the PM2.5 to PM10 ratios obtained from EPA monitoring data. Long-term temporal variability has been studied on the 1992-1997 dataset from the SGP Central Facility. A significant trend has been detected in coarse mode aerosol optical thickness following the Mt. Pinatubo eruption in 1991, while the fine mode optical thickness exhibits only seasonal variations during that period.

[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.

We present the results of a scale analysis of aerosol optical thickness (AOT) variability in time and space. The standard scale analysis methods based on variance spectra, structure functions and singular measures were applied to different datasets to determine AOT scaling properties in the scale range from 0.2 to 1200 km. We used data sets from the local network of Multi-Filter Rotating Shadowband Radiometers (MFRSRs) located at the DOE ARM program's Southern Great Plains (SGP) site. This network consists of 21 instruments arrayed across approximately 55,000 square miles in north-central Oklahoma and south-central Kansas. Our analysis demonstrates that datasets from the SGP network can provide definitive information on both temporal and spatial AOT variability. We compare AOT scale properties derived from MFRSR network data with those derived from MODIS satellite aerosol retrievals over SGP. We also use MODIS retrievals over larger land and ocean areas to study large-scale AOT variability. Our analysis suggests that the variability of AOT splits into three main scale regimes. Three-dimensional turbulent transport dominates small scales (0-30 km), the influence of 2D turbulence at scales larger than 30 km makes AOT more non-stationary, and finally, the influence of the AOT boundedness at scales larger than 100 km presses AOT behavior back towards stationarity. However, the scales used in our analysis were not large enough to observe the boundedness-induced asymptotic regime in AOT variability, where any further scale increase does not change variability properties.

In this paper tradeoffs between speed and accuracy for the atmospheric correction of hyperspectral imagery are examined. Among the issues addressed are: the use of scattering calculations on a sparse spectral grid and consequent accuracy and speed tradeoffs; methods of minimizing the required number quadrature points in multiple scattering calculations; effects of the vertical profiles of aerosols and absorbing gases on atmospheric correction; and efficient approaches from including the effects of sensor variability, or imperfections, on atmospheric correction.

We present results from a preliminary analysis of the multi-instrument MFRSR dataset from ARM CART (SGP) site Extended Facilities (Northern Oklahoma and Southern Kansas). Single-instrument retrievals are performed using our previously reported direct-diffuse algorithm. Retrieval products include daily time series of the aerosol optical depth, column mean aerosol particle size, NO 2 and ozone column amounts. The distribution of MFRSR sites at the SGP (central and extended facilities) allows us to combine the retrievals from the individual instruments into series of 2D images. These 2D images facilitate the investigation of spatial and temporal variability of aerosols and trace gases. These images can be also used to evaluate satellite aerosol data products (e.g. MODIS, TOMS, EO-1).