Diurnal variations in cloud cover and cloud vertical distribution are of great importance to Earth–atmosphere system radiative budgets and climate change. However, thus far these topics have received insufficient attention, especially on the Tibetan Plateau (TP). This study focuses on the diurnal variations in total cloud cover, cloud vertical distribution, and cirrus clouds and their relationship to meteorological factors over the TP based on active and passive satellite observations, reanalysis data, and CMIP6 outputs. Our results are consistent with previous studies but provide new insights. The results show that total cloud cover peaks at 06:00–09:00 UTC, especially over the eastern TP, but the spatial and temporal distributions of clouds from different datasets are inconsistent. This could to some extent be attributed to subvisible clouds missed by passive satellites and models. Compared with satellite observations, the amplitudes of the diurnal variations in total cloud cover obtained by the reanalysis and CMIP6 models are obviously smaller. CATS can capture the varying pattern of the vertical distribution of clouds and corresponding height of peak cloud cover at middle and high atmosphere levels, although it underestimates the cloud cover of low-level clouds, especially over the southern TP. Compared with CATS, ERA5 cannot capture the complete diurnal variations in vertical distribution of clouds and MERRA-2 has a poorer performance. We further find that cirrus clouds, which are widespread over the TP, show significant diurnal variations with averaged peak cloud cover over 0.35 at 15:00 UTC. Unlike in the tropics, where thin cirrus (0.03< optical depth <0.3) dominate, opaque cirrus clouds (0.3< optical depth <3) are the dominant cirrus clouds over the TP. The seasonal and regional averaged cloud cover of opaque cirrus reaches a daily maximum of 0.18 at 11:00 UTC, and its diurnal cycle is strong positive correlation with that of 250 hPa relative humidity and 250 hPa vertical velocity. Although subvisible clouds (optical depth <0.03), which have a potential impact on the radiation budget, are the fewest among cirrus clouds over the TP, the seasonal and regional averaged peak cloud cover can reach 0.09 at 22:00 UTC, and their diurnal cycle correlates with that of the 250 hPa relative humidity, 2 m temperature, and 250 hPa vertical velocity. Our results will be helpful to improve the simulation and retrieval of total cloud cover and cloud vertical distribution and further provide an observational constraint for simulations of the diurnal cycle of surface radiation budget and precipitation over the TP region.

Dust aerosol can affect the atmospheric thermal structure and exert great melting potential on snow and ice sheets. In this study, the decadal climatology of dust‐forced radiative heating (DRH) in the atmosphere over Tibetan Plateau and its surroundings (TPS) was investigated using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model along with the CALIPSO satellite observations from 2007 to 2016. After screening out other aerosols, the vertical distribution of dust aerosol was examined to accurately assess the DRH. The net DRH showed a significant warm effect mainly by dust loading, which covers from the center of dust sources to their adjacent transport regions. The maximum value of the DRH appeared at the near‐surface, while the DRH decreased with an increase in height. The climatic average DRH at the near‐surface reached 16.8 K/month at the Taklimakan Desert (TD) and 10.8 K/month at the Gangetic Plain in spring, 13.7 K/month at the Indus Plain in summer, which is 3–3.6 times warmer than the column‐averaged DRHs. This study also found the most significant influence of dust events on the Qaidam Basin in the TPS, in which the near‐surface DRH was 4.7 K/month during spring. It is also noteworthy that the intermonth and interannual variations of the DRH highlighted the significant warming effect of dust aerosols on the atmospheric thermal structure, especially at the near‐surface. In addition, we need to pay more attention to changes in snow‐related processes influenced by absorbing aerosols and the light‐absorbing impurities deposited in snow over the TPS.

The cloud albedo in the marine subtropical stratocumulus regions plays a key role in regulating the regional energy budget. Based on 12 years of monthly data from multiple satellite datasets, the long-term, monthly and seasonal cycle of averaged cloud albedo in five stratocumulus regions were investigated to intercompare the atmosphere-only simulations between phases 5 and 6 of the Coupled Model Intercomparison Project (AMIP5 and AMIP6). Statistical results showed that the long-term regressed cloud albedos were underestimated in most AMIP6 models compared with the satellite-driven cloud albedos, and the AMIP6 models produced a similar spread as AMIP5 over all regions. The monthly averaged values and seasonal cycle of cloud albedo of AMIP6 ensemble mean showed a better correlation with the satellite-driven observations than that of the AMIP5 ensemble mean. However, the AMIP6 model still failed to reproduce the values and amplitude in some regions. By employing the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) data, this study estimated the relative contributions of different aerosols and meteorological factors on the long-term variation of marine stratocumulus cloud albedo under different cloud liquid water path (LWP) conditions. The multiple regression models can explain ∼ 65 % of the changes in the cloud albedo. Under the monthly mean LWP ≤ 65 g m⁻², dust and black carbon dominantly contributed to the changes in the cloud albedo, while dust and sulfur dioxide aerosol contributed the most under the condition of 65 g m⁻² < LWP ≤ 120 g m⁻². These results suggest that the parameterization of cloud–aerosol interactions is crucial for accurately simulating the cloud albedo in climate models.

Studies have shown that changes in cloud cover are responsible for the rapid climate warming over the Tibetan Plateau (TP) in the past 3 decades. To simulate the total cloud cover, atmospheric models have to reasonably represent the characteristics of vertical overlap between cloud layers. Until now, however, this subject has received little attention due to the limited availability of observations, especially over the TP. Based on the above information, the main aim of this study is to examine the properties of cloud overlaps over the TP region and to build an empirical relationship between cloud overlap properties and large-scale atmospheric dynamics using 4 years (2007–2010) of data from the CloudSat cloud product and collocated ERA-Interim reanalysis data. To do this, the cloud overlap parameter α , which is an inverse exponential function of the cloud layer separation D and decorrelation length scale L , is calculated using CloudSat and is discussed. The parameters α and L are both widely used to characterize the transition from the maximum to random overlap assumption with increasing layer separations. For those non-adjacent layers without clear sky between them (that is, contiguous cloud layers), it is found that the overlap parameter α is sensitive to the unique thermodynamic and dynamic environment over the TP, i.e., the unstable atmospheric stratification and corresponding weak wind shear, which leads to maximum overlap (that is, greater α values). This finding agrees well with the previous studies. Finally, we parameterize the decorrelation length scale L as a function of the wind shear and atmospheric stability based on a multiple linear regression. Compared with previous parameterizations, this new scheme can improve the simulation of total cloud cover over the TP when the separations between cloud layers are greater than 1 km. This study thus suggests that the effects of both wind shear and atmospheric stability on cloud overlap should be taken into account in the parameterization of decorrelation length scale L in order to further improve the calculation of the radiative budget and the prediction of climate change over the TP in the atmospheric models.

Current passive sensors fail to accurately identify cloud phase, thus largely limiting the quantification of radiative contributions and precipitation of different cloud phases over the Tibet Plateau (TP), especially for the mixed-phase and supercooled water clouds. By combining the 4 years of (January 2007–December 2010) cloud phase (2B-CLDCLASS-LIDAR), radiative fluxes (2B-FLXHR-LIDAR), and precipitation (2C-PRECIP-COLUMN) products from CloudSat, this study systematically quantifies the radiative contribution of cloud phases and precipitation over the TP. Statistical results indicate that the ice cloud frequently occurs during the cold season, while mixed-phase cloud fraction is more frequent during the warm season. In addition, liquid clouds exhibit a weak seasonal variation, and the relative cloud fraction is very low, but supercooled water cloud has a larger cloud distribution (the value reaches about 0.24) than those of warm water clouds in the eastern part of the TP during the warm season. Within the atmosphere, the ice cloud has the largest radiative contribution during the cold season, the mixed-phase cloud is the second most important cloud phase for the cloud radiative contribution during the warm season, and supercooled water clouds’ contribution is particularly important during the cold season. In particular, the precipitation frequency over the TP is mainly dominated by the ice and mixed-phase clouds and is larger over the southeastern part of the TP during the warm season.