Comparisons of GCM cloud cover parameterizations with cloud-resolving model explicit simulations

Abstract

Three kinds of the widely-used cloudiness parameterizations are compared with data produced from the cloud-resolving model (CRM) simulations of the tropical cloud system. The investigated schemes include those based on relative humidity (RH), the semi-empirical scheme using cloud condensate as a predictor, and the statistical scheme based on probability distribution functions (PDFs). Results show that all three schemes are successful in reproducing the timing of cloud generation, except for the RH-based scheme, in which low-level clouds are artificially simulated during cloudless days. In contrast, the low-level clouds are well simulated in the semi-empirical and PDF-based statistical schemes, both of which are close to the CRM explicit simulations. In addition to the Gaussian PDF, two alternative PDFs are also explored to investigate the impact of different PDFs on cloud parameterizations. All the PDF-based parameterizations are found to be inaccurate for high cloud simulations, in either the magnitude or the structure. The primary reason is that the investigated PDFs are symmetrically assumed, yet the skewness factors in deep convective cloud regimes are highly significant, indicating the symmetrical assumption is not well satisfied in those regimes. Results imply the need to seek a skewed PDF in statistical schemes so that it can yield better performance in high cloud simulations. cloud cover, relative humidity, statistical cloud scheme, PDFs, CRM, KWAJEX Citation: Wang X C, Liu Y M, Bao Q, et al. 2015. Comparisons of GCM cloud cover parameterizations with cloud-resolving model explicit simulations. Science China: Earth Sciences,

An evaluation of cloud vertical structure in three reanalyses against CloudSat/cloud‐aerosol lidar and infrared pathfinder satellite observations

“…Over the past decade, CloudSat/CALIPSO have been widely used for cloud investigation and model evaluation (Luo et al, ; Yan et al, ; Yan et al, ; Yamauchi et al, ). In the cloud modeling community, a great challenge remains on how to well represent subgrid‐scale cloud condensation and fractional cloudiness (Quaas, ; Wang et al, ). Almost all climate models suffer from the poor cloudiness simulation (Zhang et al, ).…”

Development of a toy column model and its application in testing cumulus convection parameterizations

“…Strong dynamic cooling was observed in the upper levels along with pronounced moisture convergence in the lower levels [8]. This case has been widely used and described in previous studies [27].…”

Cloud Response Is Significantly Biased by CMIP6 Over the Tibetan Plateau

“…In particular, we found that optically thick but lower‐top clouds (or thin high cirrus) significantly declined (or increased) with surface warming, especially under the unstable state. These highly biased cloud response of different cloud types from CMIP6 models implied that the cloud physics parameterization processes for corresponding low and high clouds (Wang, 2017; Wang et al., 2015) require further improvement (e.g., cumulus parameterization; Gu et al., 2020). Moreover, it remains unclear whether the reduction of optically thick but lower‐top clouds, such as cumulus congestus (one of major precipitation cloud types over TP, Liu et al., 2015), means that the precipitation over TP would decrease with surface warming.…”