Guo, ZY; Wang, MQ; Peng, YR; Luo, Y (2020). Evaluation on the Vertical Distribution of Liquid and Ice Phase Cloud Fraction in Community Atmosphere Model Version 5.3 using Spaceborne Lidar Observations. EARTH AND SPACE SCIENCE, 7(3), UNSP e2019EA001029.

Cloud partition between liquid and ice phases and their vertical distributions are crucial to energy budget and global climate. Liquid and ice cloud fractions simulated by Community Atmosphere Model version 5.3 and Cloud Feedback Model Intercomparison Project Observational Simulator Package version 1.4 are evaluated by comparing to satellite retrieval data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation. Model underestimates liquid cloud by 3.3%, 5.5%, and 3.1% and overestimates ice cloud by 1.5%, 6.3%, and 4.6% in high, medium, and low levels, respectively. The misclassification of liquid cloud to ice cloud occurs in all model levels, leading to an overall underestimation of supercooled liquid fraction (SLF) globally and a shift of 50% SLF line from about -20 degrees C in observation to -5 degrees C in model. Specifically, model produces excessive ice cloud in extratropics and insufficient liquid cloud in tropics at mixed-phase levels with temperature between 0 and -40 degrees C. Plain Language Summary Cloud contains water droplets and ice crystals. In temperature between 0 and -40 degrees C, supercooled water can coexist with ice particles. The ratio of liquid phase to total cloud amount and its vertical distribution could modulate the solar energy reaching the Earth's surface and impact on the global climate. We ran a global climate model and compared the liquid fraction of cloud with satellite measurements. The model produces more ice, but less liquid cloud in all vertical levels thus underestimates the liquid fraction of cloud. The bias is significant in the levels with coexisted ice and supercooled water clouds and in tropical and high-latitude regions. The deficiencies in cloud calculation scheme in model are discussed and that could shed a light on future model improvement.