Global Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales.
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Abstract Assessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.
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Abstract. Global climate models (GCMs) have advanced in many ways ascomputing power has allowed more complexity and finer resolutions. As GCMsreach storm-resolving scales, they need to be able to produce realisticprecipitation intensity, duration, and frequency at fine scales withconsideration of scale-aware parameterization. This study uses astate-of-the-art storm-resolving GCM with a nonhydrostatic dynamical core – theModel for Prediction Across Scales (MPAS), incorporated in the atmosphericcomponent (Community Atmosphere Model, CAM) of the open-source CommunityEarth System Model (CESM), within the System for Integrated Modeling of theAtmosphere (SIMA) framework (referred to as SIMA-MPAS). At uniform coarse (here, at 120 km) gridresolution, the SIMA-MPAS configuration is comparable to the standardhydrostatic CESM (with a finite-volume (FV) dynamical core) with reasonableenergy and mass conservation on climatological timescales. With thecomparable energy and mass balance performance between CAM-FV (workhorse dynamical core) and SIMA-MPAS (newly developed dynamical core), it gives confidence inSIMA-MPAS's applications at a finer resolution. To evaluate this, we focuson how the SIMA-MPAS model performs when reaching a storm-resolving scale at3 km. To do this efficiently, we compose a case study using a SIMA-MPASvariable-resolution configuration with a refined mesh of 3 km covering thewestern USA and 60 km over the rest of the globe. We evaluated the modelperformance using satellite and station-based gridded observations withcomparison to a traditional regional climate model (WRF, the WeatherResearch and Forecasting model). Our results show realistic representationsof precipitation over the refined complex terrains temporally and spatially.Along with much improved near-surface temperature, realistic topography, andland–air interactions, we also demonstrate significantly enhanced snowpackdistributions. This work illustrates that the global SIMA-MPAS atstorm-resolving resolution can produce much more realistic regional climatevariability, fine-scale features, and extremes to advance both climate andweather studies. This next-generation storm-resolving model could ultimatelybridge large-scale forcing constraints and better inform climate impactsand weather predictions across scales.more » « less