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Accounting for the diversity in El Niño Southern Oscillation (ENSO)'s spatial pattern, with the novel ENSO longitudinal index (ELI), we evaluate the simulation of its teleconnections to US winter precipitation extremes by seven global high‐resolution (HR) Earth System Models (ESM). Six (four) HR ESMs simulate the observed increase in precipitation extremes over Southwest US (Southeast US) during ELI‐defined El Niño events better than their low‐resolution counterparts, which are low‐biased. The stronger ENSO‐dependence over the Southwest US and Southeast US in those models is associated with an improved simulation of moisture flux into the regions and/or storm track activity there. HR ESMs, however, generally overestimate the increase in precipitation extremes over the Pacific‐Northwest during La Niña events. Model bias there is associated with bias in moisture transport into the region during La Niña events, which is amplified by the enhanced vertical mass fluxes in HR. Plain Language Summary El Niño Southern Oscillation (ENSO) comes in many flavors with diverse spatial pattern of ocean warming and associated heavy tropical rainfall over the deep tropical Pacific. This ENSO diversity is one of the major reasons behind our limited understanding and prediction of its global impacts, particularly on climate extremes. A recent approach to characterize the full spectrum of ENSO using a simple index shows promise in improving our understanding. Here, we use this new index to evaluate if new state‐of‐the‐art high resolution Earth System Models can capture the remote impacts of ENSO on US precipitation extremes in the winter. We find that some of these models can credibly simulate these ENSO teleconnections, across its many flavors, over the SE‐US and SW‐US, and generally improve upon their low resolution model counterparts. Key Points We evaluate seven high‐resolution (HR) Earth System Models' simulation of diverse El Niño Southern Oscillation (ENSO) teleconnections to US winter precipitation extremes Simulation of ENSO‐dependent Southwest‐US (Southeast‐US) precipitation extremes is improved in six (four) models with resolution increase This is due to improvements in ENSO dependent moisture transport, storm track activity, and vertical mass fluxes in HR models

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

Climate models are important tools for advancing understanding and prediction of tropical cyclones (TCs). Traditional global climate models, however, do not have the ability to properly simulate TC intensity due to their coarse horizontal resolution. Regional models can be run at convection‐permitting resolutions, but these models are often strongly influenced by the data used in the lateral boundary forcing, and domain choice can have a large impact on the simulation. Cloud‐resolving global climate models have demonstrated great potential for realism in TC simulations, and in this study we focus specifically on the Simple Cloud‐Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) v1 configuration. We evaluate SCREAMv1 against the observational record and the Weather Research and Forecasting (WRF) model run at a convection‐permitting resolution with Hurricane Katrina as our case study. We found that both models produced realistic simulations of Hurricane Katrina. SCREAMv1 demonstrated skill in simulating TC track, size, and intensity, while the model produced an excessive amount of precipitation. In comparison, WRF more accurately simulated TC precipitation and intensity, although the TC wind extent was smaller than the observations. Plain Language Summary Climate models are important tools for improving our understanding of tropical cyclones (TCs). Traditional global climate models, however, do not have the ability to correctly simulate TC intensity because the models are run with grids that have coarse horizontal resolutions. Regional models can be run at high horizontal resolutions, but these models are often strongly influenced by the data that feeds into the lateral edges of the model and domain choice can have a large impact on the simulation. Cloud‐resolving global climate models have demonstrated potential for realistic TC simulations, and in this study we focus specifically on the Simple Cloud‐Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) v1. We evaluate SCREAMv1 against observations and the Weather Research and Forecasting (WRF) model run at high resolution with Hurricane Katrina as our case study. We found that both models produced realistic simulations of Hurricane Katrina. SCREAMv1 demonstrated skill in simulating TC track, size, and intensity, while the model produced an excessive amount of precipitation. In comparison, WRF more accurately simulated TC precipitation and intensity, although the TC wind extent was smaller than the observations. Key Points Hurricane Katrina hindcasts are evaluated for the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) v1 and the Weather Research and Forecasting (WRF) model SCREAMv1 at 3.25 km simulated a realistic tropical cyclone (TC) with respect to track, size, and intensity, but overestimated TC rainfall WRF at 3 km simulated TC rainfall and intensity closer to observations than SCREAMv1 but the WRF TC was smaller with delayed intensification

The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low‐resolution (roughly 110 km) and high‐resolution (roughly 25 km) configurations—as established by the High Resolution Model Intercomparison Project protocol—to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The warm season water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes with resolution to the water cycle come from reductions in precipitation and evapotranspiration. The reduction in evapotranspiration reduces the biases across nearly all of the CONUS. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack. Plain Language Summary This study seeks to better understand how the U.S. DOE's Earth system model, Energy Exascale Earth System Model, simulates the conterminous United States (CONUS) water cycle. To accomplish this goal, we examine the atmosphere and land water budget terms at the watershed and seasonal space and time scales. At higher resolution and during the warm season, all of the terms in the water budget become smaller: precipitation, evapotranspiration, moisture convergence, and runoff. The reductions in evapotranspiration lead to improvements over nearly the entire CONUS, while other terms show mixed results when increasing resolution. We also examine exploratory metrics with expected resolution sensitivity—including precipitation and streamflow extremes, storm events, and snowpack—and find modest improvements. Key Points The water cycle slows down (decreased fluxes) when grid spacing has a four times refinement HR generally improves evapotranspiration, but precipitation and other terms have mixed results HR improves precipitation extremes, storm event precipitation contributions, and mountain snowpack

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

We evaluate the simulated teleconnection of El Ni˜no—Southern Oscillation (ENSO) to winter season precipitation extremes over the United States in a long (98 years) 1950 control high-resolution version (HR; 25-km nominal atmosphere model horizontal resolution) of the U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model version 1 (E3SMv1). The model bias and spatial pattern of ENSO teleconnections to mean and extreme precipitation in HR overall are similar to the low-resolution model’s (LR; 110 km) historical simulation (four-member ensemble, 1925–59). However, over the southeastern United States (SE-U.S.), HR produces stronger El Ni˜no—associated extremes, reducing LR’s model bias. Both LR and HR produce weaker than observed increase in storm track activity during El Ni˜no events there, but HR improves the ENSO-associated variability of moisture transport over SE-U.S. During El Ni˜no, stronger vertical velocities in HR produce stronger large-scale precipitation, causing larger latent heating of the troposphere that pulls in more moisture from the Gulf of Mexico into the SE-U.S. This positive feedback also contributes to the stronger mean and extreme precipitation response in HR. Over the Pacific Northwest, LR’s bias of stronger than observed La Ni˜na associated extremes is amplified in HR. Both models simulate stronger than observed moisture transport from the Pacific Ocean into the region during La Ni˜na years. The amplified HR bias there is due to stronger orographically driven vertical updrafts that create stronger large-scale precipitation, despite weaker La Ni˜na—induced storm track activity.

This study provides an overview of the coupled high‐resolution Version 1 of the Energy Exascale Earth System Model (E3SMv1) and documents the characteristics of a 50‐year‐long high‐resolution control simulation with time‐invariant 1950 forcings following the HighResMIP protocol. In terms of global root‐mean‐squared error metrics, this high‐resolution simulation is generally superior to results from the low‐resolution configuration of E3SMv1 (due to resolution, tuning changes, and possibly initialization procedure) and compares favorably to models in the CMIP5 ensemble. Ocean and sea ice simulation is particularly improved, due to better resolution of bathymetry, the ability to capture more variability and extremes in winds and currents, and the ability to resolve mesoscale ocean eddies. The largest improvement in this regard is an ice‐free Labrador Sea, which is a major problem at low resolution. Interestingly, several features found to improve with resolution in previous studies are insensitive to resolution or even degrade in E3SMv1. Most notable in this regard are warm bias and associated stratocumulus deficiency in eastern subtropical oceans and lack of improvement in El Niño. Another major finding of this study is that resolution increase had negligible impact on climate sensitivity (measured by net feedback determined through uniform +4K prescribed sea surface temperature increase) and aerosol sensitivity. Cloud response to resolution increase consisted of very minor decrease at all levels. Large‐scale patterns of precipitation bias were also relatively unaffected by grid spacing. Plain Language Summary The Energy Exascale Earth System Model (E3SM) is a relatively new fully coupled Earth system and climate model used in major international model simulation projects and mission‐defined efforts for the U.S. Department of Energy. This paper describes the first simulation of the model in its high‐resolution configuration. This higher‐resolution version is able to capture the most energetic motions in the ocean, which are poorly represented in standard resolution coupled climate models, as well as the largest of storms in the atmosphere. Evaluation of this simulation confirms the benefits of high resolution found by other models with a few notable exceptions. These discrepancies with other studies are interesting because they provide a richer understanding of how and why resolution affects model bias. Another key finding is that climate and aerosol sensitivity in E3SM is unaffected by resolution change. This affirms the usefulness of coarser‐resolution models for understanding global‐scale climate change. This study also confirms the benefits of increased resolution for studying fine‐scale features such as hurricanes and orographic precipitation. Finally, the high‐resolution version of E3SM is shown to compare favorably to its low‐resolution counterpart and to the models participating in Phase 5 of the Coupled Model Intercomparison Project. Key Points The high‐resolution E3SMv1 model was run for 50 years using 1950 forcing data according to the HighResMIP protocol Higher resolution and associated retuning improved bias relative to coarser versions of E3SMv1, particularly in ocean and sea ice metrics Aerosol and climate sensitivity were relatively unaffected by resolution change; resolution‐related tuning had a larger impact

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.

The quantity of hydrocarbon gases trapped in natural hydrate accumulations is enormous, leading to a significant interest in the evaluation of their potential as an energy source. It has been shown that large volumes of gas can be readily produced at high rates for long times from some types of methane hydrate accumulations by means of depressurization-induced dissociation, and using conventional horizontal or vertical well configurations. However, these resources are currently assessed using simplified or reduced-scale 3D or 2D production simulations. In this study, we use the massively parallel TOUGH+HYDRATE code (pT+H) to assess the production potential of a large, deep ocean hydrate reservoir and develop strategies for effective production. The simulations model a full 3D system of over 38 km 2 extent, examining the productivity of vertical and horizontal wells, single or multiple wells, and explore variations in reservoir properties. Systems of up to 2.5 M gridblocks, running on thousands of supercomputing nodes, are required to simulate such large systems at the highest level of detail. The simulations reveal the challenges inherent in producing from deep, relatively cold systems with extensive water-bearing channels and connectivity to large aquifers, mainly difficulty of achieving depressurization and the problem of enormous water production. Also highlighted are new frontiers in large-scale reservoir simulation of coupled flow, transport, thermodynamics, and phase behavior, including the construction of large meshes and the computational scaling of larger systems.

We evaluate the simulated teleconnection of El Niño Southern Oscillation (ENSO) to winter season precipitation extremes over the United States in a long (98 years) 1950-control high resolution version (HR, 25 km nominal atmosphere model horizontal resolution) of US Department of Energy’s (DOE) Energy Exascale Earth System Model version 1 (E3SMv1). Model bias and spatial pattern of ENSO teleconnections to mean and extreme precipitation in HR overall are similar to the low-resolution model’s (LR, 110 km) historical simulation (4-member ensemble, 1925-1959). However, over the Southeast US (SE-US), HR produces stronger El Niño associated extremes, reducing upon LR’s model bias. Both LR and HR produce weaker than observed increase in storm track activity during El Niño events there. But, HR improves the ENSO associated variability of moisture transport over SE-US. During El Niño, stronger vertical velocities in HR produce stronger large-scale precipitation causing larger latent heating of the troposphere that pulls in more moisture from the Gulf of Mexico into the SE-US. This positive feedback also contributes to the stronger mean and extreme precipitation response in HR. Over the Pacific Northwest, LR’s bias of stronger than observed La Niña associated extremes is amplified in HR. Both models simulate stronger than observed moisture transport from the Pacific Ocean into the region during La Niña years. The amplified HR bias there is due to stronger orographically driven vertical updrafts that create stronger large scale precipitation, despite weaker La Niña induced storm track activity.