Preprints
https://doi.org/10.5194/gmd-2022-111
https://doi.org/10.5194/gmd-2022-111
Submitted as: model evaluation paper
17 Jun 2022
Submitted as: model evaluation paper | 17 Jun 2022
Status: a revised version of this preprint is currently under review for the journal GMD.

Advancing Precipitation Prediction Using a New Generation Storm-resolving Model Framework – SIMA-MPAS (V1.0): a Case Study over the Western United States

Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda Xingying Huang et al.
  • National Center for Atmospheric Research, Boulder, CO 80305, USA

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.

Xingying Huang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-111', Anonymous Referee #1, 04 Aug 2022
  • RC2: 'Comment on gmd-2022-111', Anonymous Referee #2, 06 Aug 2022

Xingying Huang et al.

Xingying Huang et al.

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Short summary
We focus on the recent development of a state-of-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the Western United States. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.