Progress and perspective of convection and cloud parameterizations in numerical models

This article reviews the advances of convection and cloud parameterizations in numerical models, with a focus on the significant contributions of Chinese scientists in this field. It begins by outlining the evolution and development of convection parameterization, including the Kuo scheme, the moist convective adjustment scheme, the widely used mass flux schemes, and the machine learning-based schemes. It details the schemes developed and revised by Chinese scientists, as well as the resulting improvements to the numerical models by these schemes. Following this, this review delves into the progress of cloud parameterization schemes and elaborates on the achievements of Chinese scientists in constructing and improving both cloud macrophysics and microphysics schemes. At the end, the review discusses the possible future avenues in the development of convection and cloud parameterizations, highlighting the pivotal role anticipated for deep learning, and suggests pathways for the advancement of hybrid models and multiscale climate modeling methods.