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Accurately simulating clouds remains a key challenge in global climate models, primarily because cloud formation involves sub‐grid processes that are parameterized and crudely represented in models. This study examines the performance of DOE's Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) in simulating cloud properties and their spatio‐temporal distribution by comparing against satellite observations. Two horizontal resolutions of SCREAM (3 and 12 km) are examined, and both depict a realistic spatial structure of mean‐state cloud cover but underestimate its global mean magnitude. SCREAM 3 km reasonably reproduces the distribution of mean‐state cloud properties across various cloud optical thickness and cloud‐top pressure regimes, with performance comparable to CMIP5 and CMIP6 ensemble and marginally outperforming SCREAM 12 km. Still, SCREAM 3 km tends to underpredict low clouds and optically thin clouds, highlighting the need for continued improvement in representing unresolved processes. This study provides a basis for confidence in the representation of clouds in SCREAM, as simulating mean‐state clouds is a necessary prerequisite for trusting its cloud responses to changes in aerosols and greenhouse gases. Plain Language Summary It is important for global climate models to closely reproduce the amount and physical properties of clouds in the real‐world climate. This has been challenging because they have coarse resolution and need to rely on parameterizations to approximate the formation of clouds at sub‐grid scales. This study evaluates how well the DOE's high‐resolution global storm‐resolving model (the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM)) reproduces clouds in satellite observations. SCREAM produces a spatial structure of cloud fraction that closely resembles the real world but underestimates the global mean cloudiness. Overall, we find that SCREAM performs as well as most coarse resolution global climate models in simulating the distributions of cloud vertical structure and cloud reflectivity. Yet, SCREAM underestimates the amount of low‐level clouds and thin clouds, suggesting room for improvement in representing unresolved cloud processes. This study provides a basis for confidence in the representation of clouds in SCREAM, which is a key prerequisite for simulating the response of clouds in future climate. Key Points SCREAM 3 km produces a realistic spatial and vertical distribution of cloud fraction fields, but underestimates the global average values SCREAM 3 km performs comparably to conventional climate models and marginally outperforms 12 km in representing cloud properties SCREAM 3 km underestimates low and optically thin clouds, suggesting potential areas for improvement in sub‐grid process representation

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1‐Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1‐Arctic) historical simulations (1950–2014) with the uniform low‐resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large‐scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high‐resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales. Plain Language Summary Earth system models (ESMs) are essential tools for understanding the climate system and projecting future changes, but standard coarse model resolutions often fail to realistically represent regional processes and topography, particularly in the polar regions, while uniform high‐resolution grids are too computationally expensive. To address this, regionally refined meshes (RRMs) have been developed within ESMs to provide high‐resolution simulations in target areas, improving the accuracy of regional climate simulations while maintaining computational efficiency. This study looks at how well the U.S. DOE's RRM model, E3SMv2.1‐Arctic, performs in simulating Arctic climate. The RRM, which focuses on the Arctic region, is compared to a uniform low‐resolution version, as well as to observations and reanalysis data. The RRM does a better job of simulating important Arctic climate features like precipitation, atmospheric circulation, clouds, sea ice and atmospheric rivers compared to the low‐resolution model. However, it shows some seasonal temperature biases, reducing the cold bias in summer but increasing the warm bias in winter. The RRM also underestimates winter sea ice, consistent with the warm winter bias. While the study demonstrates the advantages of using high‐resolution models to better understand Arctic climate changes, it also notes that some biases remain despite the increased resolution. Key Points Evaluation of atmosphere‐land‐ocean‐ice fully coupled E3SM‐Arctic historical simulations with regionally refined meshes for the Arctic E3SM‐Arctic reduces bias in precipitation, clouds and atmospheric rivers due to better topography compared to its low‐resolution counterpart Higher resolution leads to an increased albedo feedback and more accurate sea ice area, but with faster sea ice melting due to a warm bias

Classical numerical models for the global atmosphere, as used for numerical weather forecasting or climate research, have been developed for conventional central processing unit (CPU) architectures. This hinders the employment of such models on current top-performing supercomputers, which achieve their computing power with hybrid architectures, mostly using graphics processing units (GPUs). Thus also scientific applications of such models are restricted to the lesser computer power of CPUs. Here we present the development of a GPU-enabled version of the ICON atmosphere model (ICON-A), motivated by a research project on the quasi-biennial oscillation (QBO), a global-scale wind oscillation in the equatorial stratosphere that depends on a broad spectrum of atmospheric waves, which originates from tropical deep convection. Resolving the relevant scales, from a few kilometers to the size of the globe, is a formidable computational problem, which can only be realized now on top-performing supercomputers. This motivated porting ICON-A, in the specific configuration needed for the research project, in a first step to the GPU architecture of the Piz Daint computer at the Swiss National Supercomputing Centre and in a second step to the JUWELS Booster computer at the Forschungszentrum Jülich. On Piz Daint, the ported code achieves a single-node GPU vs. CPU speedup factor of 6.4 and allows for global experiments at a horizontal resolution of 5 km on 1024 computing nodes with 1 GPU per node with a turnover of 48 simulated days per day. On JUWELS Booster, the more modern hardware in combination with an upgraded code base allows for simulations at the same resolution on 128 computing nodes with 4 GPUs per node and a turnover of 133 simulated days per day. Additionally, the code still remains functional on CPUs, as is demonstrated by additional experiments on the Levante compute system at the German Climate Computing Center. While the application shows good weak scaling over the tested 16-fold increase in grid size and node count, making also higher resolved global simulations possible, the strong scaling on GPUs is relatively poor, which limits the options to increase turnover with more nodes. Initial experiments demonstrate that the ICON-A model can simulate downward-propagating QBO jets, which are driven by wave–mean flow interaction.

A new set of standalone parameterizations is presented for simulating the injection, evolution, and radiative forcing by stratospheric volcanic aerosols against an idealized Held–Suarez–Williamson (HSW) atmospheric background in the Energy Exascale Earth System Model version 2 (E3SMv2). In this model configuration (HSW with enabled volcanism, HSW-V), sulfur dioxide (SO2) and ash are injected into the atmosphere with a specified profile in the vertical, and they proceed to follow a simple exponential decay. The SO2 decay is modeled as a perfect conversion to a long-living sulfate aerosol which persists in the stratosphere. All three species are implemented as tracers in the model framework and are transported by the dynamical core's advection algorithm. The aerosols contribute simultaneously to local heating of the stratosphere and cooling of the surface by a simple plane-parallel Beer–Lambert law applied on two zonally symmetric radiation broadbands in the longwave and shortwave ranges. It is shown that the implementation parameters can be tuned to produce realistic temperature anomaly signatures of large volcanic events. In particular, results are shown for an ensemble of runs that mimic the volcanic eruption of Mt. Pinatubo in 1991. The design requires no coupling to microphysical subgrid-scale parameterizations and thus approaches the computational affordability of prescribed aerosol forcing strategies. The idealized simulations contain a single isolated volcanic event against a statistically uniform climate, where no background aerosols or other sources of externally forced variability are present. HSW-V represents a simpler-to-understand tool for the development of climate source-to-impact attribution methods.

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing. Plain Language Summary The U.S. Department of Energy recently released version two of its Energy Exascale Earth System Model (E3SM). E3SMv2 experienced a significant evolution in many of its model components (most notably the atmosphere and sea ice models), and its supporting software infrastructure. In this work, we document the computational performance of E3SMv2 and analyze its ability to reproduce the observed climate. To accomplish this, we utilize the standard Diagnosis and Evaluation and Characterization of Klima experiments augmented with historical simulations for the period 1850–2015. We find that E3SMv2 is nearly twice as fast as its predecessor and more accurately reproduces the observed climate in a number of metrics, most notably clouds and precipitation. We also find that the model's simulated response to increasing carbon dioxide (the equilibrium climate sensitivity) is much more realistic. Unfortunately, E3SMv2 underestimates the global mean surface temperature compared to observations during the second half of historical period. Using sensitivity experiments, where forcing agents (carbon dioxide, aerosols) are selectively disabled in the model, we determine that correcting this problem would require a strong reduction in the impact of aerosols. Key Points E3SMv2 is nearly twice as fast as E3SMv1 with a simulated climate that is improved in many metrics (e.g., precipitation and clouds) Climate sensitivity is substantially lower with a more plausible equilibrium climate sensitivity of 4.0 K (compared to an unlikely value of 5.3 K in E3SMv1) E3SMv2 underestimates the warming in the late historical period due to excessive aerosol‐related forcing

Aerosol effective radiative forcing critically influences climate projections but remains poorly constrained. Using the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) v1 configuration, we assess the sensitivity of the radiative forcing due to anthropogenic aerosol changes using a simplified prescribed aerosol scheme (SPA) derived from E3SM v3. Nudged simulations at 3 and 12 km horizontal grid spacings reveal a more negative aerosol forcing than the reference 100 km E3SM v3 whence the SPA properties are derived. The resulting globally averaged aerosol forcing signal is largely due to aerosol–cloud interactions and exhibits little overall resolution sensitivity, while hints of resolution sensitivity appear regionally between the 3 and 12 km runs. While the default SPA scheme overestimates cloud droplet dependence on aerosols, parameterization adjustments in the activation process reconcile forcing estimates with the reference model. Our results demonstrate the ability to use a prescribed aerosol scheme to hold aerosol forcing to a desired strength across resolutions.

Results from the new Department of Energy super‐parameterized (SP) Energy Exascale Earth System Model (SP‐E3SM) are analyzed and compared to the traditionally parameterized E3SMv1 and previous studies using SP models. SP‐E3SM is unique in that it utilizes Graphics Processing Unit hardware acceleration, cloud resolving model mean‐state acceleration, and reduced radiation to dramatically increase the model throughput and allow decadal experiments at 100‐km external resolution. It also differs from other SP models by using a spectral element dynamical core on a cubed‐sphere grid and a finer vertical grid with a higher model top. Despite these differences, SP‐E3SM generally reproduces the behavior of other SP models. Tropical wave variability is improved relative to E3SM, including the emergence of a Madden‐Julian Oscillation and a realistic slowdown of Moist Kelvin Waves. However, the distribution of precipitation exhibits indicates an overly frequent occurrence of rain rates less than 1 mm day −1, and while the timing of diurnal rainfall shows modest improvements the signal is not as coherent as observations. A notable grid imprinting bias is identified in the precipitation field and attributed to a unique feedback associated with the interactions between the explicit cloud resolving model convection and the spectral element grid structure. Spurious zonal mean column water tendencies due to grid imprinting are quantified—while negligible for the conventionally parameterized E3SM, they become large with super‐parameterization, approaching 10% of the physical tendencies. The implication is that finding a remedy to grid imprinting will become especially important as spectral element dynamical cores begin to be combined with explicitly resolved convection. Key Points SP‐E3SM improves tropical variability like previous super‐parameterized models but shows modest improvements in the diurnal precipitation The spectral element grid leads to an imprinting bias when used with super‐parameterization and has nonnegligible effects on the climate The throughput of SP‐E3SM was increased to roughly 1.2–1.4 simulated years per day through hardware (GPU) and algorithmic acceleration

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 ∼3× of the uniform low-resolution (LR) model at 100 km but only ∼ 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.

Simulations of the recent historical period from 1950 to 2014 are conducted with E3SM‐MMF, which uses an embedded 2D cloud resolving model that runs efficiently on GPUs in place of traditional parameterizations for cloud and turbulence. Analysis of the climate and variability reveal several aspects where E3SM‐MMF produces smaller biases compared to E3SMv2, including better agreement with the observed evolution of global mean surface temperature, although the representation of ENSO is too weak and fast. Three idealized abrupt CO2 experiments were also conducted to assess climate sensitivity and feedbacks. These yield three estimates of effective climate sensitivity (4.38, 5.21, and 6.06 K), with a corresponding spread in the shortwave cloud feedbacks. These estimates are on the higher end of sensitivity estimates from CMIP ensembles, and the spread indicates substantial state‐dependent feedbacks. These results demonstrate how multiscale modeling framework (MMF) models can be used for climate relevant experiments and projections by leveraging modern GPU enabled computational platforms. The unique qualities of E3SM‐MMF shown in previous literature are largely still present, but various instances of reduced biases suggest that MMF models have utility in improving future projections. Plain Language Summary One of the largest source of uncertainty in climate projections comes from clouds, which are often represented with relatively crude parameterizations that fail to capture the rich complexity and scale interactions in the real atmosphere. The multiscale modeling framework (MMF) was designed to address the need for a model that could be used for climate scale experiments while explicitly representing clouds in a computationally efficient way. Here we present results from an Earth system model that uses this approach, along with active ocean and sea‐ice components to simulate the recent historical period and assess the biases relative to its traditionally parameterized counterpart. Many biases are improved with the MMF despite significantly less effort spent on tuning uncertain parameters, but there are also some aspects of the variability that are worse. Additional simulations with varying levels of CO2 are used to calculate effective climate sensitivity, which is higher than most traditional models. Key Points E3SM‐MMF performs well over the historical period of 1950–2014 with active ocean and sea‐ice components despite minimal tuning The representation of ENSO variability is generally too weak and too fast compared to both E3SMv2 and observations Abrupt CO2 experiments yield a wide range of effective climate sensitivity (4.38–6.06 K) indicating substantial state‐dependent feedbacks

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950–2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales.