Analyzing the Effect of Intraseasonal Meteorological Variability and Land Cover on Aerosol-Cloud Interactions During the Amazonian Biomass Burning Season

High resolution aerosol, cloud, water vapor, and atmospheric profile data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are utilized to examine the impact of aerosols on clouds during the Amazonian biomass burning season in Rondnia, Brazil. It is found that increasing background column water vapor (CWV) throughout this transition season between the Amazon dry and wet seasons exerts a strong effect on cloud properties. As a result, aerosol-cloud correlations should be stratified by column water vapor to achieve a more accurate assessment of the effect of aerosols on clouds. Previous studies ignored the systematic changes to meteorological factors during the transition season, leading to possible misinterpretation of their results. Cloud fraction is shown generally to increase with aerosol optical depth (AOD) for both low and high values of column water vapor, whereas the relationship between cloud optical depth (COD) and AOD exhibits a different relationship. COD increases with AOD until AOD approx. 0.25 due to the first indirect (microphysical) effect. At higher values of AOD, COD is found to decrease with increasing AOD, which may be due to: (1) the inhibition of cloud development by absorbing aerosols (radiative effect) and/or (2) a retrieval artifact in which the measured reflectance in the visible is less than expected from a cloud top either from the darkening of clouds through the addition of carbonaceous biomass burning aerosols or subpixel dark surface contamination in the measured cloud reflectance. If (1) is a contributing mechanism, as we suspect, then a linear relationship between the indirect effect and increasing AOD, assumed in a majority of GCMs, is inaccurate since these models do not include treatment of aerosol absorption in and around clouds. The effect of aerosols on both column water vapor and clouds over varying land surface types is also analyzed. The study finds that the difference in column water vapor between forest and pasture is not correlated with aerosol loading, supporting the assumption that temporal variation of column water vapor is primarily a function of the larger-scale meteorology. However, a difference in the response of cloud fraction to increasing AOD is observed between forest and pasture. This suggests that dissimilarities between other meteorological factors, such as atmospheric stability, are likely to have an impact on aerosol-cloud correlations between different land-cover types

Cloud Detection And Trace Gas Retrieval From The Next Generation Satellite Remote Sensing Instruments

Thesis (Ph.D.) University of Alaska Fairbanks, 2005The objective of this thesis is to develop a cloud detection algorithm suitable for the National Polar Orbiting Environmental Satellite System (NPOESS) Visible Infrared Imaging Radiometer Suite (VIIRS) and methods for atmospheric trace gas retrieval for future satellite remote sensing instruments. The development of this VIIRS cloud mask required a flowdown process of different sensor models in which a variety of sensor effects were simulated and evaluated. This included cloud simulations and cloud test development to investigate possible sensor effects, and a comprehensive flowdown analysis of the algorithm was conducted. In addition, a technique for total column water vapor retrieval using shadows was developed with the goal of enhancing water vapor retrievals under hazy atmospheric conditions. This is a new technique that relies on radiance differences between clear and shadowed surfaces, combined with ratios between water vapor absorbing and window regions. A novel method for retrieving methane amounts over water bodies, including lakes, rivers, and oceans, under conditions of sun glint has also been developed. The theoretical basis for the water vapor as well as the methane retrieval techniques is derived and simulated using a radiative transfer model

Developing and testing a coupled regional modeling system for establishing an integrated modeling and observational framework for dust aerosol

To this date, estimates of the climate response to mineral dust remain largely uncertain because of our limited capability to quantify dust distribution in the atmosphere. Focusing on the Central and East Asian dust source regions, this thesis aims to develop a coupled regional dust modeling system to provide an improved modeling capability of atmospheric dust as well as to aid the integration of ground-based and satellite observations. The objectives of this study are as follows: 1) evaluate the capabilities of the available data to detect and quantify mineral dust in the atmosphere; 2) develop and test a coupled regional dust modeling system able to simulate size resolved dust concentrations accounting for the regional specifics of Central and East Asia; and 3) outline a methodology for data and modeling integration. The capabilities of ground-based and satellite data to characterize dust in the atmosphere are examined in great details. Based on analysis of MODIS data reflectance and radiances, we found evidence for regional signature of dust in near-IR and proposed a new probabilistic dust-cloud mask that explicitly takes into account the spatial variability characteristics of dust aerosols. We developed a coupled regional dust modeling system (WRF-DuMo) by incorporating a dust emission module (DuMo) into the NCAR WRF model. The WRF-DuMo unique capabilities include explicit treatment of land surface properties in Central and East Asia, a suite of dust emission schemes with different levels of complexity, multiple options for dust injection in the atmosphere and flexible parameters of the initial size distribution of emitted dust. Two representative dust events that originated in East Asia in the springs of 2001 and 2007 have been modeled with WRF-DuMo. Simulations with different initial size distribution of dust, injection and emission parameterizations have been performed to investigate their relative role on the modeled dust fields. We performed an integrated analysis of modeled dust fields and satellite observations by introducing an ensemble model dust index, which used in conjunction with satellite dust retrievals improves the capability to characterize dust fields. Finally, we provide recommendations for the development of an integrated observational and modeling dust framework.Ph.D.Committee Chair: Sokolik, Irina; Committee Member: Curry, Judith; Committee Member: Kalashnikova, Olga; Committee Member: Nenes, Athanasios; Committee Member: Stieglitz, Mar

Radiative Effects of African Dust and Smoke Observed from CERES and CALIOP Data

Cloud and aerosol effects have a significant impact on the atmospheric radiation budget in the Tropical Atlantic because of the spatial and temporal extent of desert dust and smoke from biomass burning in the atmosphere. The influences of African dust and smoke aerosols on cloud radiative properties over the Tropical Atlantic Ocean were analyzed for the month of July for three years (2006-2008) using collocated data collected by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Clouds and the Earth s Radiant Energy System (CERES) instruments on the CALIPSO and Aqua satellites. Aerosol layer height and type can be more accurately determined using CALIOP data, through parameters such as cloud and aerosol layer height, optical depth and depolarization ratio, than data from atmospheric imagers used in previous cloud-aerosol interaction studies. On average, clouds below 5 km had a daytime instantaneous shortwave (SW) radiative flux of 270.2 +/- 16.9 W/sq m and thin cirrus clouds had a SW radiative flux of 208.0 +/- 12.7 W/sq m. When dust aerosols interacted with clouds below 5 km, as determined from CALIPSO, the SW radiative flux decreased to 205.4 +/- 13.0 W/sq m. Similarly, smoke aerosols decreased the SW radiative flux of low clouds to a value of 240.0 +/- 16.6 W/sq m. These decreases in SW radiative flux were likely attributed to the aerosol layer height and changes in cloud microphysics. CALIOP lidar observations, which more accurately identify aerosol layer height than passive instruments, appear essential for better understanding of cloud-aerosol interactions, a major uncertainty in predicting the climate system

Observational bounds on atmospheric heating by aerosol absorption: Radiative signature of transatlantic dust

[1] Aerosols absorb solar radiation thus changing the atmospheric temperature profile but the overall magnitude of this effect is not known. To that end, Saharan dust emissions over the Atlantic Ocean provide an opportunity to examine aerosol‐related heating via satellite imaging. A major difficulty, however, is disentangling a straightforward heating signal caused by the absorbing dust from a meteorological signal, which originates from correlation between dust concentration and air temperature. To tackle the problem, we combine temperature (T) soundings, from the atmospheric infrared sounder (AIRS), with aerosol optical depth (τ) measurements, from the moderate resolution imaging spectroradiometer (MODIS), and data assimilation results from the global data assimilation system (GDAS). We introduce the quantity β(P) ≡ ∂TP/∂τ, the subscript indicating temperature at a given pressure, and study the observed (AIRS) vs. modeled (GDAS) vertical profiles of β(P). Using the vertical as well as horizontal patterns of β(P) and Δβ(P) ≡ βobs. − βmodl., we avoid instrumental and geographic artifacts and extract a remarkably robust radiative heating signal of about 2–4 K within the dust layer. The extracted signal peaks over the mid‐Atlantic Ocean, as a result of competing trends: “memory” of the dust source in the east, and mixing with transparent aerosol in the west

Statistical Analysis and Comparison of Optical Classification of Atmospheric Aerosol Lidar Data

This dissertation presents a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and also consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear models (GLM) and regression tree techniques are used to further analyze the performance of the LIDAR parameter-based aerosol classification methods. The goal of this part of GLM and regression tree analyses is to compare and contrast distinct classification data schemes, and compare the results with the measured aerosol reflection data in the atmosphere. The detail statistical comparison and analysis show that the optical methods adopted in this study for classification and prediction of various harmful aerosol types such as soot, carbon monoxide (CO), sulfates (SOx) and nitrates (NOx) are effective

Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

The Along-Track Scanning Radiometers (ATSRs) provide a long time-series of measurements suitable for the retrieval of cloud properties. This work evaluates the freely-available Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) dataset (version 3) created from the ATSR-2 (1995�2003) and Advanced ATSR (AATSR; 2002 onwards) records. Users are recommended to consider only retrievals flagged as high-quality, where there is a good consistency between the measurements and the retrieved state (corresponding to about 60% of converged retrievals over sea, and more than 80% over land). Cloud properties are found to be generally free of any significant spurious trends relating to satellite zenith angle. Estimates of the random error on retrieved cloud properties are suggested to be generally appropriate for optically-thick clouds, and up to a factor of two too small for optically-thin cases. The correspondence between ATSR-2 and AATSR cloud properties is high, but a relative calibration difference between the sensors of order 5�10% at 660 nm and 870 nm limits the potential of the current version of the dataset for trend analysis. As ATSR-2 is thought to have the better absolute calibration, the discussion focusses on this portion of the record. Cloud-top heights from GRAPE compare well to ground-based data at four sites, particularly for shallow clouds. Clouds forming in boundary-layer inversions are typically around 1 km too high in GRAPE due to poorly-resolved inversions in the modelled temperature profiles used. Global cloud fields are compared to satellite products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, and a climatology of liquid water content derived from satellite microwave radiometers. In all cases the main reasons for differences are linked to differing sensitivity to, and treatment of, multi-layer cloud systems. The correlation coefficient between GRAPE and the two MODIS products considered is generally high (greater than 0.7 for most cloud properties), except for liquid and ice cloud effective radius, which also show biases between the datasets. For liquid clouds, part of the difference is linked to choice of wavelengths used in the retrieval. Total cloud cover is slightly lower in GRAPE (0.64) than the CALIOP dataset (0.66). GRAPE underestimates liquid cloud water path relative to microwave radiometers by up to 100 g m�2 near the Equator and overestimates by around 50 g m�2 in the storm tracks. Finally, potential future improvements to the algorithm are outlined

Investigating Aerosol Effects on Clouds, Precipitation and Regional Climate in US and China by Means of Ground-based and Satellite Observations and a Global Climate Model

Aerosols affect climate by scattering/absorbing radiation and by acting as cloud condensation nuclei (CCN) or ice nuclei (IN). One of the least understood but most significant aspects of climate change is the aerosol effect on cloud and precipitation. A hypothesis has recently been proposed that, in addition to reducing cloud effective radius and suppressing precipitation, aerosols may also modify the thermodynamic structure of deep convective clouds and lead to enhanced precipitation when complex thermodynamic processes are involved. Taking advantage of the long-term and extensive ground-based observations at the US Department of Energy's Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, we thoroughly tested such a hypothesis and provide direct evidence of it. Moreover, the hypothesis is also supported by analysis of satellite-based observations over tropical regions from multiple sensors in the A-Train satellites constellation. Extensive analyses of the long-term ground-based and large-scale data reveal significant increases in rain rate or frequency and cloud top heights with increasing aerosol loading for mix-phase deep convective clouds, decreases rain frequency for low liquid clouds, but little impact on cloud height for liquid clouds. Rigorous tests are conducted to investigate any potential artifacts and influences of meteorological conditions. Large-scale circulation patterns and monsoon systems can be changed by scattering and absorption of solar radiation by aerosols. By means of model simulations with the National Center for Atmospheric Research Community Climate Model (NCAR/CCM3), we found that the increase of aerosol loading in China contributes to circulation changes, leading to more frequent occurrence of fog events in winter as observed from meteorological records. The increase in atmospheric aerosols over China heats the atmosphere and generates a cyclonic circulation anomaly over eastern-central China. This circulation anomaly leads to a reduction in the influx of dry and cold air over that area during winter. Weakening of the East Asian winter monsoon system may also contribute to these changes. All these changes favor the formation and maintenance of fog over this region. The MODerate resolution Imaging Spectroradiometer (MODIS) aerosol products used in the above studies are validated using ground-based measurements from the Chinese Sun Hazemeter Network (CSHNET). Overall, substantial improvement was found in the current version of aerosol products relative to the previous one. At individual sites, the improvement varies with surface and atmospheric conditions

Investigating Elevated Aqua Modis Aerosol Optical Depth Retrievals Over The Mid-Latitude Southern Oceans

A band of elevated aerosol optical depth (AOD) over the mid-latitude Southern Oceans has been identified in some passive satellite-based aerosol datasets such as Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) products. In this study, Aqua MODIS (AM) aerosol products in this zonal region are investigated in detail to assess retrieval accuracy. This is done through multiple data sets, including spatially and temporally collocated cloud and aerosol products produced by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) project for investigating AM AOD in this region with respect to lidar profiling of cloud presence. Maritime Aerosol Network (MAN) and Aerosol Robotic Network (AERONET) AOD data are also collocated with AM for surface context. The results of this study suggest that the apparent high AOD belt, seen in some satellite aerosol products based on passive remote sensing methods, is not seen in the CALIOP aerosol product based on an active remote sensing technique with an enhanced cloud detection capability and is not detected from ground-based observations such as MAN and AERONET data. The apparent high AOD belt, although largely attributed to stratocumulus and low broken cumulus cloud contamination as suggested by CALIOP products, could not be fully credited to cloud contamination. Collocated CALIOP data also suggest that the current cloud screening methods implemented in the over ocean AM aerosol products are ineffective in identifying cirrus clouds. Cloud residuals still exist in the AM AOD products even with the use of the most stringent cloud screening settings