ALBERT

Description: 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 of aerosol properties at these locations. In this study, we present trend analysis of AERONET data at 63 selected locations. In addition to commonly studied parameters such as Aerosol Optical Depth (AOD) and Ångström Exponent (AE), we also focus on Absorption Aerosol Optical Depth (ABS), Scattering Optical Depth (SCT), Single Scattering Albedo (SSA) and the Absorption Ångström Exponent (AAE). Two statistical methods are used to detect and estimate the trend: Mann–Kendall test associated with Sen's slope and linear least square fitting. Their results 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 and SCT, while significant (〉 90%) increases of these two parameters are found for Kanpur, India. Most of European and North American sites also show negative trends for ABS, as well as three East Asian stations. The reduction in ABS results in positive SSA trends for these locations. The increase of SCT also leads to a positive SSA trend for Kanpur. Negative SSA trends are mostly found over South America, Australia and a few West European stations, which are mainly attributed to the increase of absorption. Fewer stations are found with significant trends for AE and AAE. In general, the trends do not exhibit obvious seasonality for the majority of the parameters and stations.

Description: In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and ground-based observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the cross-covariance matrix between the joint satellite data field and Aerosol Robotic Network (AERONET) station data, both parallel comparison across different satellite data sets and the evaluation of the satellite data against the AERONET measurements are simultaneously realized. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but also identifies the strengths and weaknesses of each data set in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different data sets, and in evaluating the performance of different data sets on seasonal and interannual timescales. Global results indicate that different data sets agree qualitatively for major aerosol-source regions. Discrepancies are mostly found over the Sahel, India, eastern and southeastern Asia. Results for eastern Europe suggest that the intense wildfire event in Russia during summer 2010 was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to misclassification of smoke plumes as clouds. Analysis for the Indian subcontinent shows that here SeaWiFS agrees best with AERONET in terms of seasonality for both the Gangetic Basin and southern India, while on interannual timescales it has the poorest agreement.

Description: 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.

Description: The development of remote sensing techniques has greatly advanced our knowledge of atmospheric aerosols. Various satellite sensors and the associated retrieval algorithms all add to the information of global aerosol variability, while well-designed surface networks provide time series of highly accurate measurements at specific locations. In studying the variability of aerosol properties, aerosol climate effects, and constraining aerosol fields in climate models, it is essential to make the best use of all of the available information. In the previous three parts of this series, we demonstrated the usefulness of several spectral decomposition techniques in the analysis and comparison of temporal and spatial variability of aerosol optical depth using satellite and ground-based measurements. Specifically, Principal Component Analysis (PCA) successfully captures and isolates seasonal and interannual variability from different aerosol source regions, Maximum Covariance Analysis (MCA) provides a means to verify the variability in one satellite dataset against Aerosol Robotic Network (AERONET) data, and Combined Principal Component Analysis (CPCA) realized parallel comparison among multi-satellite, multi-sensor datasets. As the final part of the study, this paper introduces a novel technique that integrates both multi-sensor datasets and ground observations, and thus effectively bridges the gap between these two types of measurements. The Combined Maximum Covariance Analysis (CMCA) decomposes the cross covariance matrix between the combined multi-sensor satellite data field and AERONET station data. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol source regions and events represented by different satellite datasets, but also identifies the strengths and weaknesses of each dataset in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different datasets, and in evaluating the performance of different datasets on seasonal and interannual time scales.