Comment on gmd-2022-111

Abstract

Global climate models (GCMs) have advanced in many ways as computing power has allowed more complexity and finer resolution. As GCMs reach storm-resolving scale, for predictions to be useful, they need to be able to produce realistic precipitation distributions and intensity at fine scales. This study uses a state-of-art global storm-resolving GCM, the System for Integrated Modeling of the Atmosphere (SIMA), as the atmospheric component of the open-source Community Earth System Model (CESM) and a non-hydrostatic dynamical core – the Model for Prediction Across Scales (MPAS). For mean climatology, at uniform coarse (here, at 120 km) grid-resolution, the SIMA-MPAS configuration is comparable to the standard hydrostatic CESM (with finite-volume (FV) dynamical core) with reasonable energy and mass conservation. We mainly investigate how the SIMA-MPAS model performs when reaching storm-resolving scale at 3 km. To do this effectively, we compose a case study using a SIMA-MPAS variable resolution configuration with a refined mesh of 3 km covering the western US and 60 km remaining of the globe. Our results show realistic representations of precipitation details over the refined complex terrains temporally and spatially. Along with much improved temperature features from well performed land-air interactions and realistic topography, we also demonstrate significantly enhanced snowpack distributions. We compared and evaluated the model performance using both observations and a traditional regional climate model. This work illustrates that a global SIMA-MPAS at storm resolving resolution can produce much more realistic regional climate variability, fine-scale features, and extremes to advance both climate and weather studies. The next-generation storm-resolving model could ultimately bridge large-scale forcing constraints and better-informed climate impacts and weather predictions across scales.