Global Health and Climate Effects of Organic Aerosols from Different Sources

The impact of aerosols on human health and climate is well-recognized, yet many studies have only focused on total PM2.5 or changes from anthropogenic activities. This study quantifies the health and climate effects of organic aerosols (OA) from anthropogenic, biomass burning, and biogenic sources. Using two atmospheric chemistry models, CAM-chem and GEOS-Chem, our findings reveal that anthropogenic primary OA (POA) has the highest efficiency for health effects but the lowest for direct radiative effects due to spatial and temporal variations associated with population and surface albedo. The treatment of POA as nonvolatile or semivolatile also influences these efficiencies through different chemical processes. Biogenic OA shows moderate efficiency for health effects and the highest for direct radiative effects but has the lowest efficiency for indirect effects due to the reduced high cloud, caused by stabilized temperature profiles from aerosol-radiation interactions in biogenic OA-rich regions. Biomass burning OA is important for cloud radiative effect changes in remote atmospheres due to its ability to be transported further than other OAs. This study highlights the importance of not only OA characteristics such as toxicity and refractive index but also atmospheric processes such as transport and chemistry in determining health and climate impact efficiencies.

Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction models

Weather prediction is essential to the daily life of human beings. Current numerical weather prediction models such as the Global Forecast System (GFS) are still subject to substantial forecast biases and rarely consider the impact of atmospheric aerosol, despite the consensus that aerosol is one of the most important sources of uncertainty in the climate system. Here we demonstrate that atmospheric aerosol is one of the important drivers biasing daily temperature prediction. By comparing observations and the GFS prediction, we find that the monthly-averaged bias in the 24-h temperature forecast varies between ± 1.5 °C in regions influenced by atmospheric aerosol. The biases depend on the properties of aerosol, the underlying land surface, and aerosol-cloud interactions over oceans. It is also revealed that forecast errors are rapidly magnified over time in regions featuring high aerosol loadings. Our study provides direct "observational" evidence of aerosol's impacts on daily weather forecast, and bridges the gaps between the weather forecast and climate science regarding the understanding of the impact of atmospheric aerosol.

Aerosols optical depth and Ångström exponent over different regions in Garhwal Himalaya, India

Aerosol optical depth (AOD) and Ångström exponent (AE) are observed to be important parameters in understanding the status of ambient aerosol concentration over a particular location and depend not only upon the local but also on the large-scale dynamics of the atmosphere. The present article analyses the AOD and AE parameters retrieved with Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments onboard satellites, for the upper (Chamoli) and foothill (Dehradun) regions of Garhwal Himalaya in Uttarakhand, India, from 2006 to 2015. Aerosol properties are investigated at monthly, seasonal, and annual scales. The monthly mean values of MODIS-derived AOD and AE were observed to be 0.18 (± 0.14) and 1.05 (± 0.43) respectively over the Dehradun region. The seasonal maximums in AOD with MODIS and MISR were observed as 0.23 ± 0.06 and 0.29 ± 0.07 respectively in the pre-monsoon season, and the minimum values (0.099 ± 0.02) were observed in the post-monsoon season, over the Dehradun region. In contrast, in the Chamoli region, the maximum AOD (MODIS) was 0.21 ± 0.06 observed in the monsoon season and the minimum was 0.036 ± 0.007 in the post-monsoon season. Over a decade, the AE for Chamoli and Dehradun was found to vary from 0.07 to 0.17 and from 0.14 to 0.20 respectively. The median AE for Chamoli and Dehradun was found to be 1.49 and 1.47 respectively, marking the dominance of fine mode particles of anthropogenic origin. Observations show the presence of dust and polluted dust resulting from the long-range transport from the west. The comparison of AOD values from the two sensors shows a significant correlation (0.73) with slightly higher values from MISR over the year. The results obtained are important in understanding the climatic implications due to the atmospheric aerosols over the abovementioned Himalayan region of Uttarakhand, India.

The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.

High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS

The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce Level 2 aerosol data at varying spatial resolutions (1, 3, and 10 km) and Level 3 data at 1 degree. The local and global applications have benefited from the coarse resolution gridded data sets (i.e., Level 3, 1 degree), as it is easier to use since data volume is low, and several online and offline tools are readily available to access and analyze the data with minimal computing resources. At the same time, researchers who require data at much finer spatial scales have to go through a challenging process of obtaining, processing, and analyzing larger volumes of data sets that require high-end computing resources and coding skills. Therefore, we created a high spatial resolution (high-resolution gridded (HRG), 0.1 × 0.1 degree) daily and monthly aerosol optical depth (AOD) product by combining two MODIS operational algorithms, namely Deep Blue (DB) and Dark Target (DT). The new HRG AODs meet the accuracy requirements of Level 2 AOD data and provide either the same or more spatial coverage on daily and monthly scales. The data sets are provided in daily and monthly files through open an Ftp server with python scripts to read and map the data. The reduced data volume with an easy to use format and tools to access the data will encourage more users to utilize the data for research and applications.