Recent global nonhydrostatic modeling approach without using a cumulus parameterization to understand the mechanisms underlying cloud changes due to global warming

Abstract Clouds are the primary source of uncertainty in the prediction of climate change. To reduce the uncertainty of cloud simulations and overcome this difficulty in prediction, many climate modeling centers are now developing a new type of climate model, the global nonhydrostatic atmospheric model, which reduces the uncertainty arising from a cumulus parameterization by computing clouds explicitly using a cloud microphysics scheme. Among the global nonhydrostatic atmospheric models used in recent intercomparison studies, NICAM aims to project climate change by improving our understanding of cloud changes due to warming and related physical processes. NICAM is the first global nonhydrostatic model and was developed by our research team. This review summarizes the outcomes of a recent major five-year research program in Japan for studying climate using NICAM, as well as providing an overview of current issues regarding the use of global kilometer-scale simulations in high-resolution climate modeling