[A-AS05] Large-scale moisture and organized cloud systems

[AAS05-P08] Deep Numerial Analysis (DNA) climate project: Creating a framework for the matching between short and long time-scale climate simulations

*Hiroaki Miura¹, Chihiro Kodama², Yuki Takano³, Daisuke Takasuka³, Tamaki Suematsu⁴ (1.Graduate School of Science, The University of Tokyo, 2.Japan Agency for Marine-Earth Science and Technology, 3.Atmosphere Ocean Research Institute, The University of Tokyo, 4.RIKEN Center for Computational Science)

Keywords:Climate, GCRM, GCM

We have launched the “DNA (Deep Numerical Analysis) Climate Science” project to develop an earth systems model with a kilometer-scale horizontal mesh size, as part of the basic technology for a “digital earth.” Through this project, we will realize a new climate simulation using a numerical model, in which the hierarchical structures of the clouds inherent in typhoons and Madden-Julian oscillations are spontaneously reproduced according to cloud microphysics equations. This is analogous to the spontaneous formation of cells and organs in living organisms according to DNA, the blueprint of life. For this purpose, the “global cloud-resolving model” (GCRM) and “global climate model” (GCM), which have been developed independently for over 20 years, will be integrated to utilize their respective strengths. The GCRM can physically express cloud microphysics and the interactions between radiation and circulation, and has been particularly adept at reproducing short-term phenomena. The GCM has reduced climate bias by utilizing convection and cloud parameterization and has become an essential tool for research on understanding and predicting climate change. Furthermore, it has contributed to the IPCC assessment reports for many years. In this project, we will construct a new generation of climate models that will bring qualitative reform to climate research, through asymptotic matching of solutions between GCRM and GCM on the time scales of seasons to several years, which both have large uncertainties.