Evaluation and comparison of CMIP6 models and MERRA-2 reanalysis AOD against Satellite observations from 2000 to 2014 over China

Rapid industrialization and urbanization along with a growing population are contributing significantly to air pollution in China. Evaluation of long-term aerosol optical depth (AOD) data from models and reanalysis, can greatly promote understanding of spatiotemporal variations in air pollution in China. To do this, AOD (550 nm) values from 2000 to 2014 were obtained from the Coupled Model Inter-comparison Project (CIMP6), the second version of Modern-Era Retrospective analysis for Research, and Applications (MERRA-2), and the Moderate Resolution Imaging Spectroradiometer (MODIS; flying on the Terra satellite) combined Dark Target and Deep Blue (DTB) aerosol product. We used the Terra-MODIS DTB AOD (hereafter MODIS DTB AOD) as a standard to evaluate CMIP6 Ensemble AOD (hereafter CMIP6 AOD) and MERRA-2 reanalysis AOD (hereafter MERRA-2 AOD). Results show better correlations and smaller errors between MERRA-2 and MODIS DTB AOD, than between CMIP6 and MODIS DTB AOD, in most regions of China, at both annual and seasonal scales. However, significant under- and over-estimations in the MERRA-2 and CMIP6 AOD were also observed relative to MODIS DTB AOD. The long-term (2000–2014) MODIS DTB AOD distributions show the highest AOD over the North China Plain (0.71) followed by Central China (0.69), Yangtse River Delta (0.67), Sichuan Basin (0.64), and Pearl River Delta (0.54) regions. The lowest AOD values were recorded over the Tibetan Plateau (0.13 ± 0.01) followed by Qinghai (0.19 ± 0.03) and the Gobi Desert (0.21 ± 0.03). Large amounts of sand and dust particles emitted from natural sources (the Taklamakan and Gobi Deserts) may result in higher AOD in spring compared to summer, autumn, and winter. Trends were also calculated for 2000–2005, for 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan or FYP), and for 2011–2014 (during the 12th FYP). An increasing trend in MODIS DTB AOD was observed throughout the country during 2000–2014. The uncontrolled industrialization, urbanization, and rapid economic development that mostly occurred from 2000 to 2005 probably contributed to the overall increase in AOD. Finally, China's air pollution control policies helped to reduce AOD in most regions of the country; this was more evident during the 12th FYP period (2011–2014) than during the 11th FYP period (2006–2010). Therefore this study strongly advises the authority to retain or extend these policies in the future for improving air quality

Seasonal Aerosol Classification Over South Asia by Satellite based Atmospheric Optical Data

Aerosol optical characteristics have been investigated to explore regional and seasonal inconsistencies of aerosols and to define the dominant type throughout South Asia from 2001 to 2021. MODIS aerosol products from collection 6.1 have been used in present study, that comprise daily values of Angstrom exponent (AE) and aerosol optical depth (AOD) data. MODIS-derived AODs are validated by using nine ground-based AERONET station data. Overall, an adequate correlation is found among the two datasets. However, an overestimation of the MODIS retrievals is found in one site named Jaipur and underestimations are found at two sites named as Gandhi-college and Karachi. The seasonal evaluation shows that aerosol distribution found between 0 and 1.05, depending on the change in geographical location. The highest AOD value originates over the Indo-Gangetic plain (IGP), mostly throughout warm season. The second maximum AOD value covers a large area of South Asia during spring, summer and autumn. The lowest values of AOD are found in winter season excluding the IGP. A region with high aerosol optical depth (AOD) values support a low value of angstrom exponent (AE) indicating the coarse aerosol during warm seasons (spring and summer) over IGP. The region with high AOD and high AE values is showing fine aerosol during the mild to cold seasons (autumn and winter). The threshold values for AOD and AE have been used to classify aerosols. The results demonstrate that urban/industrial aerosols prominent in every season across the region dominate in spring and summer due to frequent occurrence of dust events. The mixed type aerosol is second largest contributor in aerosol formation in all seasons. The Biomass burning/smoke aerosol is dominant over IGP due to open forest and crop burning in autumn. Clean and maritime aerosol has small unnoticeable involvement in the studied region

Estimating PM2.5 Concentrations Using 3 KM MODIS AOD Products: A Case Study in British Columbia, Canada

PM2.5 refers to fine particles with diameters smaller than 2.5 μm. The rising level of PM2.5 reveals adverse effects on climate change, economic losses, international conflicts, and public health. Exposure to the high level of PM2.5 would increase the risk of premature death, especially for people with weak immune systems, such as children and elder people. The main sources of PM2.5 include combustion of biomass, vehicle and industrial emissions, and wildfire smoke. British Columbia (BC), Canada, with a land area of 944,735 km2 and 27 regional districts, experienced its record-breaking wildfire season in 2017. However, due to the uneven distribution of PM2.5 ground monitoring stations in BC, PM2.5 concentrations in the rural area are difficult to retrieve. Remote sensing techniques and geographical information systems (GIS) could be used as supplementary tools to estimate PM2.5 concentrations. Aerosol Optical Depth (AOD) has been proven to have a strong correlation with PM2.5. Moderate Resolution Imaging Spectroradiometer (MODIS) provides AOD products in both 3 km and 10 km resolutions. The 3 km MODIS AOD products were released in 2013, and have been widely used to estimate PM2.5 concentrations in several studies. This study adopted the 3 km Aqua MODIS AOD products to estimate PM2.5 concentrations in BC in the year of 2017 by combining ground station measurements, meteorological and supplementary data. MODIS AOD products were validated with ground-level AErosol RObotic NETwork (AERONET) AOD data. The Multiple Linear Regression (MLR) model, Geographically Weighted Regression (GWR) model, and a novel theoretical model were then conducted to estimate PM2.5 concentrations by integrating MODIS AOD products, ground-level PM2.5 concentrations, meteorological and supplementary data. After comparing the performance of the three models, the GWR model was used to generate annual, seasonal, and monthly spatial distribution maps of PM2.5. The application feasibility of MODIS AOD products in predicting PM2.5 was also examined. The validation results showed that there was a strong correlation between the MODIS AOD and the AERONET AOD. The GWR model had the best prediction performance, while the MLR generated the worst prediction results. After analyzing the spatial distribution maps of PM2.5 with ground-level PM2.5 distribution maps, it could be concluded that the PM2.5 concentrations estimated by the GWR model almost follow the same trend as ground station measured PM2.5. In addition, PM2.5 concentrations were the highest in summer and August based on the estimation results of seasonal and monthly GWR models. It indicated that the application feasibility of MODIS AOD products in predicting PM2.5 concentrations during BC’s wildfire season was high

AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA)

Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20th March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, instead providing information on aerosol amount and size. The purpose is to introduce AEROSA for those researchers who are interested in the generic classification of aerosols based on AOD and AE, without claiming the exact aerosol types such as DD, BB, CC, and CM. AEROSA not only provides 9 generic aerosol classes for all observations but can also accommodate variations in location and season, which GA aerosol types do not.</jats:p

Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties

Global Validation of MODIS C6 and C6.1 Merged Aerosol Products over Diverse Vegetated Surfaces

In this study, the MODerate resolution Imaging Spectroradiometer (MODIS) Collections 6 and 6.1 merged Dark Target (DT) and Deep Blue (DB) aerosol products (DTBC6 and DTBC6.1) at 0.55 µm were validated from 2004–2014 against Aerosol Robotic Network (AERONET) Version 2 Level 2.0 AOD obtained from 68 global sites located over diverse vegetated surfaces. These surfaces were categorized by static values of monthly Normalized Difference Vegetation Index (NDVI) observations obtained for the same time period from the MODIS level-3 monthly NDVI product (MOD13A3), i.e., partially/non–vegetated (NDVIP ≤ 0.3), moderately–vegetated (0.3 &lt; NDVIM ≤ 0.5) and densely–vegetated (NDVID &gt; 0.5) surfaces. The DTBC6 and DTBC6.1 AOD products are accomplished by the NDVI criteria: (i) use the DT AOD retrievals for NDVI &gt; 0.3, (ii) use the DB AOD retrievals for NDVI &lt; 0.2, and (iii) use an average of the DT and DB AOD retrievals or the available one with highest quality assurance flag (DT: QAF = 3; DB: QAF ≥ 2) for 0.2 ≤ NDVI ≤ 0.3. For comparison purpose, the DTBSMS AOD retrievals were included which were accomplished using the Simplified Merge Scheme, i.e., use an average of the DTC6.1 and DBC6.1 AOD retrievals or the available one for all the NDVI values. For NDVIP surfaces, results showed that the DTBC6 and DTBC6.1 AOD retrievals performed poorly over North and South America in terms of the agreement with AERONET AOD, and over Asian region in terms of retrievals quality as the small percentage of AOD retrievals were within the expected error (EE = ± (0.05 + 0.15 × AOD). For NDVIM surfaces, retrieval errors and poor quality in DTBC6 and DTBC6.1 were observed for Asian, North American and South American sites, whereas good performance, was observed for European and African sites. For NDVID surfaces, DTBC6 does not perform well over the Asian and North American sites, although it contains retrievals only from the DT algorithm which was developed for dark surfaces. Overall, the performance of the DTBC6.1 AOD retrievals was significantly improved compared to the DTBC6, but still more improvements are required over NDVIP, NDVIM and NDVID surfaces of Asia, NDVIM and NDVID surfaces of North America, and NDVIM surfaces of South America. The performance of the DTBSMS retrievals was better than the DTBC6 and DTBC6.1 retrievals with 11–13% (31%) greater number of coincident observations, 6–9% (14–22%) greater percentage of retrievals within the EE, and 30–100% (46–100%) smaller relative mean bias compared to the DTBC6.1 (DTBC6) at a global scale

Global validation of MODIS C6 and C6.1 merged aerosol products over diverse vegetated surfaces

201809 bcrcVersion of RecordPublishe

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe