GTS v1.0: A Macrophysics Scheme for Climate Models Based on a Probability Density Function [post]

Chein-Jung Shiu, Yi-Chi Wang, Huang-Hsiung Hsu, Wei-Ting Chen, Hua-Lu Pan, Ruiyu Sun, Yi-Hsuan Chen, Cheng-An Chen
2020 unpublished
Abstract. Cloud macrophysics schemes are unique parameterizations for general circulation models. We propose an approach based on a probability density function (PDF) that utilizes cloud condensates and saturation ratios to replace the assumption of critical relative humidity (RH). We test this approach, called the GFS-TaiESM-Sundqvist (GTS) scheme, using the macrophysics scheme within the Community Atmospheric Model version 5.3 (CAM5.3) framework. Via single-column model results, the new
more » ... ults, the new approach reveals a stronger linear relationship between the cloud fraction (CF) and RH when compared to that of the default CAM5.3 scheme. We also validate the impact of the GTS scheme on global climate simulations with satellite observations. The simulated CF is comparable to CloudSat/CALIPSO data. Comparisons of the vertical distributions of CF and cloud water content (CWC), as functions of large-scale dynamic and thermodynamic parameters, with the CloudSat/CALIPSO data suggest that the GTS scheme can closely simulate observations. This is particularly noticeable for thermodynamic parameters, such as RH, upper-tropospheric temperature, and total precipitable water, implying that our scheme can simulate variation in CF associated with RH more reliably than the default scheme. Changes in CF and CWC would affect climatic fields and large-scale circulation via cloud–radiation interactions. Both climatological means and annual cycles of many of the GTS-simulated variables are improved compared with the default scheme, particularly with respect to water vapor and RH fields. Different PDF shapes in the GTS scheme also significantly affect global simulations.
doi:10.5194/gmd-2020-144 fatcat:lg5gb5n6fraddgy3dwtv7ukjnu