Advancing precipitation prediction using a new-generation storm-resolving model framework – SIMA-MPAS (V1.0): a case study over the western United States
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Published:2022-11-11
Issue:21
Volume:15
Page:8135-8151
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Huang Xingying, Gettelman AndrewORCID, Skamarock William C., Lauritzen Peter Hjort, Curry Miles, Herrington Adam, Truesdale John T., Duda Michael
Abstract
Abstract. Global climate models (GCMs) have advanced in many ways as
computing power has allowed more complexity and finer resolutions. As GCMs
reach storm-resolving scales, they need to be able to produce realistic
precipitation intensity, duration, and frequency at fine scales with
consideration of scale-aware parameterization. This study uses a
state-of-the-art storm-resolving GCM with a nonhydrostatic dynamical core – the
Model for Prediction Across Scales (MPAS), incorporated in the atmospheric
component (Community Atmosphere Model, CAM) of the open-source Community
Earth System Model (CESM), within the System for Integrated Modeling of the
Atmosphere (SIMA) framework (referred to as SIMA-MPAS). At uniform coarse (here, at 120 km) grid
resolution, the SIMA-MPAS configuration is comparable to the standard
hydrostatic CESM (with a finite-volume (FV) dynamical core) with reasonable
energy and mass conservation on climatological timescales. With the
comparable energy and mass balance performance between CAM-FV (workhorse dynamical core) and SIMA-MPAS (newly developed dynamical core), it gives confidence in
SIMA-MPAS's applications at a finer resolution. To evaluate this, we focus
on how the SIMA-MPAS model performs when reaching a storm-resolving scale at
3 km. To do this efficiently, we compose a case study using a SIMA-MPAS
variable-resolution configuration with a refined mesh of 3 km covering the
western USA and 60 km over the rest of the globe. We evaluated the model
performance using satellite and station-based gridded observations with
comparison to a traditional regional climate model (WRF, the Weather
Research and Forecasting model). Our results show realistic representations
of precipitation over the refined complex terrains temporally and spatially.
Along with much improved near-surface temperature, realistic topography, and
land–air interactions, we also demonstrate significantly enhanced snowpack
distributions. This work illustrates that the 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. This next-generation storm-resolving model could ultimately
bridge large-scale forcing constraints and better inform climate impacts
and weather predictions across scales.
Funder
National Science Foundation
Publisher
Copernicus GmbH
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