Aerosols have a significant effect on the global radiation budget through their interactions with radiation and clouds. However, estimates of their effect are the dominant source of uncertainty in current estimates of total anthropogenic effect on climate. A major cause of this uncertainty is the high degree of variability of aerosol properties and processes that affect their lifetime. Prediction of the aerosol effect on climate depends on the ability of three-dimensional numerical models to accurately estimate aerosol properties. However, a limitation of traditional grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid-boxes, which can lead to discrepancies between observations and aerosol models. This thesis uses a synthesis of aerosol observations, global climate model (GCM) data, and a new aerosol modelling technique implemented within a regional-scale model to quantify the important scales of aerosol variability and the extent to which different sub-grid scale processes contribute to discrepancies in aerosol modelling. Analysis of black carbon (BC) plumes from aircraft observations shows that BC plumes represent a large portion of total BC mass and typically exist on scales of 65{ 100 km. Comparison of observed plume scales to those simulated by GCMs at multiple resolutions show that GCMs overestimate the scales of along- ight-track variability by 64% at the highest resolution. Variability is shown to be greater near sources than in remote regions, indicating that models may benefit from higher resolutions in regions of high emissions. Additionally, GCMs at all resolutions show higher variability in the latitudinal direction than the longitudinal direction, suggesting that capturing latitudinal variability may result in greater improvements in aerosol modelling. This work additionally presents a novel technique to allow one to isolate the effect of aerosol variability from other sources of variability within the model. Processes most affected by neglecting aerosol sub-grid variability are gas-phase chemistry and aerosol uptake of water through the aerosol/gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol parameters when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. Future aerosol model development should focus on accounting for the effect of sub-grid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.

Earth's radiation budget is directly influenced by aerosols through the absorption of solar radiation and subsequent heating of the atmosphere. Aerosols modulate the hydrological cycle indirectly by modifying cloud properties, precipitation and ocean heat storage. In addition, polluting aerosols impose health risks in local, regional and global scales. In spite of recent advances in the study of aerosols variability, uncertainty in their spatial and temporal distributions still presents a challenge in the understanding of climate variability. For example, aerosol loading varies not only from year to year but also on higher frequency intraseasonal time scales producing strong variability on local and regional scales. An assessment of the impact of aerosol variability requires long period measurements of aerosols at both regional and global scales. The present dissertation compiles a large database of remotely sensed aerosol loading in order to analyze its spatio-temporal variability, and how this load interacts with different variables that characterize the dynamic and thermodynamic states of the environment. Aerosol Index (AI) and Aerosol Optical Depth (AOD) were used as measures of the atmospheric aerosol load. In addition, atmospheric and oceanic satellite observations, and reanalysis datasets is used in the analysis to investigate aerosol-environment interactions. A diagnostic study is conducted to produce global and regional aerosol satellite climatologies, and to analyze and compare the validity of aerosol retrievals. We find similarities and differences between the aerosol distributions over various regions of the globe when comparing the different satellite retrievals. A nonparametric approach is also used to examine the spatial distribution of the recent trends in aerosol concentration. A significant positive trend was found over the Middle East, Arabian Sea and South Asian regions strongly influenced by increases in dust events. Spectral and composite analyses of surface temperature, atmospheric wind, geopotential height, outgoing longwave radiation, water vapor and precipitation together with the climatology of aerosols provide insight on how the variables interact. Different modes of variability, especially in intraseasonal time scales appear as strong modulators of the aerosol distribution. In particular, we investigate how two modes of variability related to the westward propagating synoptic African Easterly Waves of the Tropical Atlantic Ocean affect the horizontal and vertical structure of the environment. The statistical significance of these two modes is tested with the use of two different spectral techniques. The pattern of propagation of aerosol load shows good correspondence with the progression of the atmospheric and oceanic synoptic conditions suitable for dust mobilization over the Atlantic Ocean. We present extensions to previous studies related with dust variability over the Atlantic region by evaluating the performance of the long period satellite aerosol retrievals in determining modes of aerosol variability. Results of the covariability between aerosols-environment motivate the use of statistical regression models to test the significance of the forecasting skill of daily AOD time series. The regression models are calibrated using atmospheric variables as predictors from the reanalysis variables. The results show poor forecasting skill with significant error growing after the 3rd day of the prediction. It is hypothesized that the simplicity of linear models results in an inability to provide a useful forecast.

The Moderate-resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Terra and Aqua satellites have provided a global distribution of aerosols. The space-based inversion of MODIS measurements requires assumption about the surface and aerosol properties, both are highly heterogeneous in space and time. This thesis has investigated the role of surface reflectance and aerosol properties on the retrieval of aerosols from MODIS over the Indian region. The aerosol properties retrieved by MODIS including total aerosol optical depth (AOD) and aerosol fine mode fraction (AFMF, fractional contribution of fine mode aerosols in the total AOD) were compared with that obtained from Aerosol Robotic Network (AERONET) at Kanpur (26.45◦N,80.35◦E), Indo-Gangetic Basin, northern India. This region is a special region for the study of aerosols as it offers strong aerosol seasonality, where the region is influenced by dust aerosols during pre-monsoon (March to June) and dominated by the fine mode particles in winter (November to February). The MODIS Collection 004 (C004) aerosol products systematically overestimated AOD in the presence of dust and underestimated when fine particles were dominant. The errors in the retrieval of dust AOD were correlated with the apparent reflectance at 2.1 µm, from which the surface reflectance in the visible channels (0.47 µm and 0.66 µm) were estimated using the “dark target” spectral correlation method. The error in the retrieval of AOD were also found to be large in the scattering angle range 120◦150◦, where the scattering properties of the non-spherical dust aerosols differ from that of the assumed spherical particles. AFMF of C004 was found to be highly biased to fine mode at Kanpur. The Collection 005 (C005) aerosol retrieval of the second-generation aerosol algorithm, however, showed improved retrieval of spectral AOD, which is likely to be attributed to the use of updated aerosol models and parameterized surface reflectance. In contrast to the C004 products, fine AOD and fine-model weighting (FMW) of C005 were biased very low at Kanpur and also over the greater Indian land region. This has indicated that the inversion of the space-based MODIS measurements is non-unique in which an improper combination of surface reflectance and aerosol model provide more accurate retrieval of the total aerosol optical depth. The surface reflectance relationships between the visible and shortwave-infrared 2.1 µm channels derived from the actual measurements of the surface reflectance using a spectroradiometer onboard an aircraft over Bangalore (12.95◦N,77.65◦E) in the southern India were found to have higher slope and intercept than that assumed by the MODIS algorithm over the same region. The high spectral correlations between the measured reflectance at longer wavelengths indicated some potential to estimate the surface reflectance at these wavelengths which needs further investigation. An experiment on the retrieval of aerosols carried out with several combinations of aerosol models and visible surface reflectance clearly shown that the surface reflectance in the visible channels assumed in the MODIS aerosol algorithm should be increased from its current parameterization in order to retrieve more accurate total as well as size-segregated aerosol optical properties at Kanpur and also over the greater Indian land region. In addition to the visible channels, inclusion of longer wavelengths in the aerosol inversion would likely improve the accuracy of retrieval over land by resolving the spectral dependence of aerosols. This in turn can help in separating the anthropogenic and natural aerosols in the total aerosol loading.