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This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations - a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25→100¯km atmosphere and land, a 0.125 river-routing model, and 14→60¯km ocean and sea ice. By design, the computational cost of NARRM is 1/43× of the uniform low-resolution (LR) model at 100¯km but only 1/4¯10¯%-20¯% of a globally uniform high-resolution model at 25¯km. A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air-sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land-atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations.

This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110-km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model's strong aerosol-related effective radiative forcing (ERFari+aci = −1.65 W/m2) and high equilibrium climate sensitivity (ECS = 5.3 K).

Climate simulation with more accurate process-level representation at finer resolutions is a pressing need in order to provide actionable information to policy-makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building, and running high-resolution (HR, here 0.25° average grid spacing at the equator) models (HRM). A more affordable path to HRM is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low-resolution (LR, 1°). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations, as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces both well- and poorly simulated behaviours of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behaviour of physical parameterizations, especially for variables influencing sub-grid scale physical processes. RRM can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a testbed, and as such represents an efficient and more robust physics testbed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective approach for HRM development and hydrologic research. [ABSTRACT FROM AUTHOR]