Abstract
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.
Highlights
Introduction published maps and institutional affilCloud cover plays an important role in climatic systems, having a significant effect on the radiation budget and corresponding water cycles [1,2,3,4]
The total cloud fraction at a given grid-box is defined as the ratio of the number of cloudy profiles to the number of all sample profiles, while the relative cloud fraction (RCF) for each cloud phase is calculated based on the Equation (1)
The total cloud fraction at a given grid-box is6 of de21 fined as the ratio of the number of cloudy profiles to the number of all sample profiles, while the RCF for each cloud phase is calculated based on the Equation (1)
Summary
Introduction published maps and institutional affilCloud cover plays an important role in climatic systems, having a significant effect on the radiation budget and corresponding water cycles [1,2,3,4]. It is crucial to understand the main characteristics and physical processes of clouds, such as various macro-physical effects (e.g., cloud fraction (CF), cloud thickness) and microphysical properties (e.g., cloud droplet number concentration, particle size, phase), as well as the complicated dynamical [5,6] and microphysical processes [7]. Due to an incomplete understanding of physical processes, processes related to clouds have been poorly represented in climate and weather models [8]; this lack of research has been identified as the greatest source of uncertainty in climate predictions driven by climate models [9,10]. The cloud radiative effect and precipitation properties have resulted in findings which vary due to cloud phase changes [11,12,13,14,15].
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