Abstract. Using 2B-CLDCLASS-LIDAR (radar–lidar) cloud classification and 2B-FLXHR-LIDAR radiation products from CloudSat over 4 years, this study evaluates the co-occurrence frequencies of different cloud types, analyzes their along-track horizontal scales and cloud radiative effects (CREs), and utilizes the vertical distributions of cloud types to evaluate cloud-overlap assumptions. The statistical results show that high clouds, altostratus (As), altocumulus (Ac) and cumulus (Cu) tend to coexist with other cloud types. However, stratus (St) (or stratocumulus, Sc), nimbostratus (Ns) and convective clouds are much more likely to exhibit individual features than other cloud types. On average, altostratus-over-stratus/stratocumulus cloud systems have a maximum horizontal scale of 17.4 km, with a standard deviation of 23.5 km. Altocumulus-over-cumulus cloud types have a minimum scale of 2.8 km, with a standard deviation of 3.1 km. By considering the weight of each multilayered cloud type, we find that the global mean instantaneous net CREs of multilayered cloud systems during the daytime are approximately −41.3 and −50.2 W m−2, which account for 40.1 and 42.3% of the global mean total net CREs at the top of the atmosphere (TOA) and at the surface, respectively. The radiative contributions of high-over-altocumulus and high-over-stratus/stratocumulus (or cumulus) in the all multilayered cloud systems are dominant due to their frequency. Considering the overlap of cloud types, the cloud fraction based on the random overlap assumption is underestimated over vast oceans, except in the west-central Pacific Ocean warm pool. Obvious overestimations mainly occur over tropical and subtropical land masses. In view of a lower degree of overlap than that predicted by the random overlap assumption to occur over the vast ocean, particularly poleward of 40° S, the study therefore suggests that a linear combination of minimum and random overlap assumptions may further improve the predictions of actual cloud fractions for multilayered cloud types (e.g., As + St/Sc and Ac + St/Sc) over the Southern Ocean. The establishment of a statistical relationship between multilayered cloud types and the environmental conditions (e.g., atmospheric vertical motion, convective stability and wind shear) would be useful for parameterization design of cloud overlap in numerical models.

Abstract. Based on 8 years of (January 2008-December 2015 cloud phase information from the GCM-Oriented Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud Product (GOCCP), aerosol products from CALIPSO and meteorological parameters from the ERA-Interim products, the present study investigates the effects of atmospheric dynamics on the supercooled liquid cloud fraction (SCF) during nighttime under different aerosol loadings at global scale to better understand the conditions of supercooled liquid water gradually transforming to ice phase.Statistical results indicate that aerosols' effect on nucleation cannot fully explain all SCF changes, especially in those regions where aerosols' effect on nucleation is not a first-order influence (e.g., due to low ice nuclei aerosol frequency). By performing the temporal and spatial correlations between SCFs and different meteorological factors, this study presents specifically the relationship between SCF and different meteorological parameters under different aerosol loadings on a global scale. We find that the SCFs almost decrease with increasing of aerosol loading, and the SCF variation is closely related to the meteorological parameters but their temporal relationship is not stable and varies with the different regions, seasons and isotherm levels. Obviously negative temporal correlations between SCFs versus vertical velocity and relative humidity indicate that the higher vertical velocity and relative humidity the smaller SCFs. However, the patterns of temporal correlation for lower-tropospheric static stability, skin temperature and horizontal wind are relatively more complex than those of vertical velocity and humidity. For example, their close correlations are predominantly located in middle and high latitudes and vary with latitude or surface type. Although these statistical correlations have not been used to establish a certain causal relationship, our results may provide a unique point of view on the phase change of mixed-phase cloud and have potential implications for further improving the parameterization of the cloud phase and determining the climate feedbacks.

Abstract. 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.

The accurate representation of cloud vertical overlap in atmospheric models is particularly significant for predicting the total cloud cover and for the calculations related 25 to the radiative budget in these models. However, it has received too little attention due to the limited observation, especially over the Tibetan Plateau (TP). In this study, 4 years stability on cloud overlap should both be taken into account in the parameterization of overlap parameter to improve the simulation of total cloud cover in models.

<p><strong>Abstract.</strong> Based on the 4 years (2007–2010) of data from the CloudSat 2B-CLDCLASS-LIDAR product, the European Centre for Medium-Range Weather Forecasts Auxiliary (ECMWF-AUX) product and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) level 2 5 km aerosol layer product, this study investigates the impact of atmospheric dynamics and aerosol on cold cloud (cloud top temperature < 0 ºC) phase on a global scale in order to better understand the conditions under which supercooled liquid water will gradually transform to ice phase. <br><br> Our results show that the thresholds of parameter <i>T</i><sub>ice</sub> (is the temperature below which all clouds are ice), <i>T</i><sub>w</sub> (is the temperature above which all clouds are liquid) and <i>n</i> (is a shape parameter that controls the relationship between supercooled liquid cloud fraction (SCF) and cloud top temperature) aren't unique for the entire globe as many models adopted. The value of <i>T</i><sub>w</sub> ranges from −2 ºC to −6 ºC at the most regions of the globe, and decreases from high latitudes to tropics. For <i>T</i><sub>ice</sub>, its value is warmer (>−26 ºC) in the typical stratocumulus regions than the values at the other regions (<30 ºC). The geographic distributions of parameter <i>n</i> are closely linked to aerosol loading and meteorological parameters, and its value varies strongly from 0 to 5. By comparing the absolute and relative differences between different cloud phase schemes and observation, we suggest that the cloud phase scheme used in Community Atmosphere Model (version 3, CAM3) and CAM5 can be considered as a preferred option in the models, and the application of dynamic thresholds of <i>T</i><sub>ice</sub>, <i>T</i><sub>w</sub> and <i>n</i> will further improve the predictions of SCF, particularly over the region of poleward of 40°. <br><br> Statistical results indicate that aerosol effect on nucleation can't fully explain the all changes of cold cloud phase in our study. SCF at a given temperature also appears to be related to the different collocations of surface temperature, vertical velocity and lower-tropospheric static stability (LTSS). We find that strong vertical motion can also enhance glaciation process and reduce the SCF (or increase <i>n</i> value) as ice nuclei aerosol did, and force the supercooled water to glaciate at a warmer temperature. For same vertical motion, however, high LTSS (or low surface temperature) tends to increase the SCF and force the supercooled water to glaciate at a colder temperature. Unstable atmosphere (low LTSS and high surface temperature) in those strong ascent regions favors deep convective cloud, and further exhausts the supercooled water by strong precipitation rate. Our results verify the importance and regional of dynamical factors on the changes of cold cloud phase, have potential implications for further improving the parameterization of the cloud phase and determining the climate feedbacks.</p>