The seasonal and spatial variations of vertical distribution and optical properties of aerosols over China are studied using long-term satellite observations from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) and ground-based lidar observations and Aerosol Robotic Network (AERONET) data. The CALIOP products are validated using the ground-based lidar measurements at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL). The Taklamakan Desert and Tibetan Plateau regions exhibit the highest depolarization and color ratios because of the natural dust origin, whereas the North China Plain, Sichuan Basin and Yangtze River Delta show the lowest depolarization and color ratios because of aerosols from secondary formation of the anthropogenic origin. Certain regions, such as the North China Plain in spring and the Loess Plateau in winter, show intermediate depolarization and color ratios because of mixed dust and anthropogenic aerosols. In the Pearl River Delta region, the depolarization and color ratios are similar to but higher than those of the other polluted regions because of combined anthropogenic and marine aerosols. Long-range transport of dust in the middle and upper troposphere in spring is well captured by the CALIOP observations. The seasonal variations in the aerosol vertical distributions reveal efficient transport of aerosols from the atmospheric boundary layer to the free troposphere because of summertime convective mixing. The aerosol extinction lapse rates in autumn and winter are more positive than those in spring and summer, indicating trapped aerosols within the boundary layer because of stabler meteorological conditions. More than 80 % of the column aerosols are distributed within 1.5 km above the ground in winter, when the aerosol extinction lapse rate exhibits a maximum seasonal average in all study regions except for the Tibetan Plateau. The aerosol extinction lapse rates in the polluted regions are higher than those of the less polluted regions, indicating a stabilized atmosphere due to absorptive aerosols in the polluted regions. Our results reveal that the satellite and ground-based remote-sensing measurements provide the key information on the long-term seasonal and spatial variations in the aerosol vertical distribution and optical properties, regional aerosol types, long-range transport and atmospheric stability, which can be utilized to more precisely assess the direct and indirect aerosol effects on weather and climate.

The lack of complete knowledge concerning the complex interactions among clouds, circulation, and climate hinders our ability to simulate the Earth’s climate correctly. This study contributes to a broader understanding of the implications of cloud and precipitation biases on the representation of coupled energy and water exchanges by bringing together a suite of cloud impact parameters (CIPs). These parameters measure the coupled impact of cloud systems on regional energy balance and hydrology by simultaneously capturing the absolute strength of the cloud albedo and greenhouse effects, the relative importance of these two radiative effects, and the efficiency of precipitating clouds to radiatively heat the atmosphere and cool the surface per unit of heating through rain production. Global distribution of these CIPs is derived using satellite observations from CloudSat and used to evaluate energy and water cycle coupling in four reanalysis datasets [both versions of the Modern-Era Retrospective Analysis for Research and Applications (MERRA and MERRA-2); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Japanese 55-year Reanalysis (JRA-55)]. The results show that the reanalyses provide a more accurate representation of the three radiation-centric parameters than the radiative efficiencies. Of the four reanalyses, MERRA and ERA-Interim provide the best overall representation of the different cloud processes but can still show significant biases. JRA-55 exhibits some clear deficiencies in many parameters, while MERRA-2 seems to introduce biases that were not evident in MERRA.

The tracking of carbon and aerosol co-emissions is essential for environmental management. Satellite-based atmospheric synoptic observation networks provide large-scale and multifaceted data to help resolve emission behaviors. This study employs a comprehensive analysis of atmospheric dynamics, combustion byproducts, and aerosol characteristics around power plants. Strong correlations between Aerosol Optical Depth (AOD) at 500 nm and the column-averaged dry-air mole fraction of carbon dioxide (XCO₂) were observed, revealing synchronous peaks in their emission patterns. The investigation into combustion completeness utilized metrics such as the ratio of carbon monoxide (CO)/XCO₂ and Black Carbon Extinction (BCEXT)/Total Aerosol Extinction (TOTEXT). Discrepancies in these ratios across cases suggest variations in combustion efficiency and aerosol characteristics. Nitrogen dioxide (NO₂) distributions closely mirrored XCO₂, indicating consistent emission patterns, while variations in sulfur dioxide (SO₂) distributions implied differences in sulfide content in the coal used. The influence of coal composition on AOD/XCO₂ ratios was evident, with sulfide content contributing to variations besides combustion efficiency. This multifactorial analysis underscores the complex interplay of combustion completeness, aerosol composition, and coal components in shaping the air quality around power stations. The findings highlight the need for a nuanced understanding of these factors for effective air quality management.

Understanding the connections between latent heating from precipitation and cloud radiative effects is essential for accurately parameterizing cross-scale links between cloud microphysics and global energy and water cycles in climate models. Although commonly examined separately, this study adopts two cloud impact parameters (CIPs), the surface radiative cooling efficiency R_c and atmospheric radiative heating efficiency R_h, that explicitly couple cloud radiative effects and precipitation to characterize how efficiently precipitating cloud systems influence the energy budget and water cycle using A-Train observations and two reanalyses. These CIPs exhibit distinct global distributions that suggest cloud energy and water cycle coupling are highly dependent on cloud regime. The dynamic regime ω₅₀₀ controls the sign of R_h, whereas column water vapor (CWV) appears to be the larger control on the magnitude. The magnitude of R_c is highly coupled to the dynamic regime. Observations show that clouds cool the surface very efficiently per unit rainfall at both low and high sea surface temperature (SST) and CWV, but reanalyses only capture the former. Reanalyses fail to simulate strong R_h and moderate R_c in deep convection environments but produce stronger R_c and R_h than observations in shallow, warm rain systems in marine stratocumulus regions. Although reanalyses generate fairly similar climatologies in the frequency of environmental states, the response of R_c and R_h to SST and CWV results in systematic differences in zonal and meridional gradients of cloud atmospheric heating and surface cooling relative to A-Train observations that may have significant implications for global circulations and cloud feedbacks. Significance Statement: Studying climate change requires understanding coupled interactions between clouds, precipitation, and their environment. Here we calculate two parameters to reveal how efficiently clouds can heat the atmosphere or cool the surface per unit rain. The satellite observations and reanalyses show similar global patterns, but there are some differences in areas of deep convection and low cloud regions. Examination of these parameters as a function of their environment shows that reanalyses cool the atmosphere too much per unit rain in environments with low sea surface temperatures and water vapor. Vertical velocity determines whether clouds heat or cool the atmosphere. Both observations and reanalyses suggest that water vapor is the stronger control on how much clouds heat the atmosphere per unit rain.