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The long-term global distribution study of Aerosol Optical Depth (AOD) has great significance for air pollution control, forest fire prevention and climate studies. The global spatial-temporal distribution and variation of the AOD at 550 nm are analyzed using AOD 550 product of Modis/Aqua during 2003–2021. The AOD 550 is also studied by Empirical Orthogonal Function (EOF) analysis method. The results show that the distribution of AOD 550 has obvious regional and seasonal characteristics, and the interannual variation is distinct. The AOD 550 distribution with latitude/longitude is closely related to the distribution of population and aerosol natural source. The high-AOD 550 areas are mainly distributed in eastern Asia, southeast Asia, south Asia, the Middle East, northwestern Africa, central and western Africa, western waters of Africa and the Amazon region of South America; The AOD 550 seasonal distribution results are JJA > MAM > DJF > SON over globe, SON > DJF > (MAM ≈ JJA) over the southern hemisphere, and MAM > JJA > DJF > SON over the northern hemisphere, respectively. Moreover, the seasonal distribution characteristics varies in different regions especially over ocean and land. The results of interannual variation show an upward trend during 2005–2011, and fluctuates during 2012–2020. The annual mean AOD 550 over the northern hemisphere (0.187–0.207) is significantly higher than that of southern hemisphere (0.110–0.135), while the latter shows an increasing trend (0.0009/year). The EOF analysis results demonstrate the complexity of the AOD 550 global distribution. The annual mean results show the change trends of aerosols in different regions and find some special aerosol events. While the monthly mean results show that aerosols over ocean are mostly dominated by land sources (dust and biomass burning sources), and the ocean transport pattern presents an annual cycle. • The global distribution of Aerosol Optical Depth is studied using MODIS level 3 data from 2003 to 2021. • The seasonal and interannual variations of Aerosol Optical Depth are reported with the global perspective. • The Empirical Orthogonal Function analysis of Aerosol Optical Depth is performed. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is a pivotal region for investigating climate change and the emission of greenhouse gases in terrestrial ecosystems. In this study, atmospheric methane columnar concentration (XCH 4) data obtained from GOSAT products were used from March 2010 to February 2022. The comparative study between GOSAT XCH 4 and Waliguan (WLG) ground-based methane measurements in annual terms demonstrated a high consistency, with a correlation coefficient of 0.996, indicating similar trends and amplitudes. Subsequently, a comprehensive investigation was carried out to explore the long-term trends and spatial differences of XCH 4 on the TP, along with its association with climate variables. The findings revealed an east-west gradient in the spatial distribution of XCH 4 , characterized by higher concentrations in the southeast and lower concentrations in the northwest. During the 12-year study period, a consistent upward trend of XCH 4 was exhibited across the whole plateau, ranging from 7.53 to 10.15 ppb/yr, and the areas of exceeding 9 ppb/yr accounted for 80.4% of the study area. The seasonal variation of XCH 4 displayed a typical unimodal distribution, with the highest XCH 4 occurring in late August to early September. The interannual growth rate of XCH 4 was the highest in summer, followed by autumn, winter, and the lowest in spring. To better understand the climate variables influencing XCH 4 on the TP, a Principal Component Analysis (PCA) was performed with a biplot. The results indicated that temperature (TEM), precipitation (PER), soil temperature (Soil_TEM), and soil humidity (Soil_HUM) were positively correlated with XCH 4. However, wind speed (WSPD) and wind direction (WDIR) demonstrated negative correlations with XCH 4. • XCH 4 exhibits a pronounced increasing trend in Tibetan Plateau during 2010–2022. • The spatial distribution of XCH 4 and its interannual growth rates indicate strong regional signals and seasonal cycle. • Principal Component Analysis (PCA) reveals different correlations between XCH 4 and climate factors. [ABSTRACT FROM AUTHOR]