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Supported by the U.S. Department of Energy (DOE), the Energy Exascale Earth System Model (E3SM) project aims to optimize the use of DOE resources to address the grand challenge of actionable predictions of Earth system variability and change. This requires sustained advancement to (1) integrate model development with leading‐edge computational advances toward ultra‐high‐resolution modeling; (2) represent the coupled human‐Earth system to address energy sector vulnerability to variability and change; and (3) address uncertainty in model simulations and projections. Scientific development of the E3SM modeling system is driven by the simulation requirements in three overarching science areas centering on understanding the Earth's water cycle, biogeochemistry, and cryosphere systems and their future changes. This paper serves as an introduction to the E3SM special collection, which includes 50 papers published in several AGU journals. It provides an overview of the E3SM project, including its goals and science drivers. It also provides a brief history of the development of E3SM version 1 and highlights some key findings from papers included in the special collection. Plain Language Summary Earth system models are important tools for predicting future changes in the Earth system. Supported by the U.S. Department of Energy (DOE), the Energy Exascale Earth System Model (E3SM) project aims to improve predictions of Earth system variability and change. The latter has a particular focus on predicting changes in the water cycle that influences precipitation and storms, biogeochemistry that influences greenhouse gases and future warming, and cryosphere systems that influence sea‐level rise that threatens coastal communities. This paper serves as an introduction to the E3SM special collection, which includes 50 papers published in several AGU journals. It provides an overview of the E3SM project, including its goals and science drivers. It also provides a brief history of the development of E3SM version 1 and highlights some key findings from papers included in the special collection. Key Points The Energy Exascale Earth System Model (E3SM) project aims to improve actionable predictions of Earth system variability and change Water cycle, biogeochemistry, and cryosphere systems are the three key science drivers of model development and numerical experiments This paper provides an overview of the E3SM project and introduces the E3SM special collection of papers published in AGU journals

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 presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1° x 1.25° finite-volume current-climate Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average precipitation, 2 m temperature, and snowpack are performed. The regional model reproduces the spatial distribution of precipitation quite well, but substantially overestimates rainfall along windward slopes. This is due to strong overprediction of precipitation intensity; precipitation frequency is actually underpredicted by the model. Moisture fluxes impinging on the coast seem to be well-represented over California, implying that precipitation bias is caused by processes internal to WRF. Positive-definite moisture advection and use of the Grell cumulus parameterization result in some decrease in precipitation bias, but other sources are needed to explain the full bias magnitude. Surface temperature is well simulated in all seasons except summer, when overly-dry soil moisture results in a several degree warm bias in both CCSM3 and WRF. Additionally, coastal temperatures appear to be too warm due to a coastal sea surface temperature bias inherited from CCSM3. Modeled snowfall/snowmelt agrees quite well with observations, but snow water equivalent is found to be much too low due to monthly reinitialization of all regional model fields from CCSM3 values.

Climate simulations with more accurate process-level representation at finer resolutions (<100 km) are a pressing need in order to provide more detailed 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 (HRMs). A more affordable path to HRMs 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 the precipitation metrics 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 behavior of physical parameterizations, especially for variables influencing sub-grid-scale physical processes. RRMs 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 test bed and as such represents an efficient and more robust physics test bed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective test bed for HRM development.

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high‐resolution modeling of global‐to‐local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science‐to‐service pathways, and co‐production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services. Plain Language Summary In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities currently at hand, and working with stakeholders to co‐produce the information that they require. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services. Key Points Facing the uncharted territory of a changed climate and increasingly complex socio‐economic systems requires the best possible predictions Scientific and technological opportunities are at hand to accelerate progress on U.S. climate predictions and projections Capitalizing on the most transformative opportunities calls for an unprecedented level of coordination and resources

Many climate models exhibit a dry and warm bias over the central United States during the summer months, including the Energy Exascale Earth System Model (E3SM) and its Multiscale Modeling Framework (MMF) configuration. Understanding the causes of this bias is important to shine a light on this common model error and reduce the uncertainty in future projections. In this study, we use E3SMv2 and E3SM-MMF to assess how parameterized and resolved convection affect temperature and precipitation biases over the Southern Great Plains site of the Atmospheric Radiation Measurement program. Both configurations overestimate near-surface temperature and underestimate precipitation at the ARM SGP site. The bias is associated with a lack of low-level clouds during days without precipitation and too much incoming solar radiation causing the surface to warm. Low-level cloud fraction in E3SM-MMF during the nonprecipitating days is lower in comparison to E3SMv2 and observation, consistent with the larger warm bias. We also find that the underestimated precipitation can be characterized as “too frequent, too weak” in E3SMv2 and “too rare, too intense” in E3SM-MMF. These deficiencies conspire to sustain the warm and dry bias over the central United States.

In the Energy Exascale Earth System Model (E3SM) Multi-scale Modeling Framework (MMF), where parameterizations of convection and turbulence are replaced by a 2-D cloud-resolving model (CRM), there are multiple options to represent land–atmosphere interactions. Here, we propose three different coupling strategies, namely the (1) coupling of a single land surface model to the global grid (MMF), (2) coupling a single land copy directly to the embedded CRM (SFLX2CRM), and (3) coupling a single copy of land model to each column of the CRM grid (MAML). In the MAML (Multi-Atmosphere Multi-Land) framework, a land model is coupled to CRM at the CRM-grid scale by coupling an individual copy of a land model to each CRM grid. Therefore, we can represent intra-CRM heterogeneity in the land–atmosphere interaction processes. There are 5-year global simulations run using these three coupling strategies, and we find some regional differences but overall small changes with respect to whether a land model is coupled to CRM or a global atmosphere. In MAML, the spatial heterogeneity within CRM induces stronger turbulence, which leads to the changes in soil moisture, surface heat fluxes, and precipitation. However, the differences in the MAML from the other two cases are rather weak, suggesting that the impact of using MAML does not justify the increase in cost.

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 Weather Research and Forecasting (WRF) model version 3.0.1 is used in both short-range (days) and long-range (years) simulations to explore the California wintertime model wet bias. California is divided into four regions (the coast, central valley, mountains, and Southern California) for validation. Three sets of gridded surface observations are used to evaluate the impact of measurement uncertainty on the model wet bias. Short-range simulations are driven by the North American Regional Reanalysis (NARR) data and designed to test the sensitivity of model physics and grid resolution to the wet bias using eight winter storms chosen from four major types of large-scale conditions: the Pineapple Express, El Niño, La Niña, and synoptic cyclones. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to assess the robustness of microphysics and cumulus parameterizations to resolution changes. Additionally, long-range simulations driven by both NARR and general circulation model (GCM) data are performed at low resolution to gauge the impact of the GCM forcing on the model wet bias. These short- and long-range simulations show that low-resolution runs tend to underpredict precipitation in the coast region and overpredict it elsewhere in California. The sensitivity test of WRF physics in short-range simulations indicates that model precipitation depends most strongly on the microphysics scheme, though convective parameterization is also important, particularly near the coast. In contrast, high-resolution (2 km) simulation increases model precipitation in all regions. As a result, it improves the forecast bias in the coast region while it downgrades the model performance in the other regions. It is also found that the choice of validation dataset has a significant impact on the model wet bias of both short- and long-range simulations. However, this impact in long-range simulations appears to be a secondary contribution as compared to its counterpart from the GCM forcing.

The speakers were, in order of presentation, Warren Washington [National Center for Atmospheric Research (NCAR)], Kelly Redmond (Western Regional Climate Center), George Boer (Canadian Centre for Climate Modelling and Analysis), and Lennart Bengtsson [University of Reading, former director of the European Centre for Medium-Range Weather Forecasts (ECMWF)]. The report's recommendations were as follows: 1 Establish a national climatic research program with international coordination; 2 Establish a climatic data analysis program, to study the impact of climate change on food, water, and energy supplies; 3 Develop a climatic index monitoring program . . . a national watchdog for climate change; 4 Establish a climatic modeling and applications program (CMAP), and explore possible future climates using coupled general circulation models (GCMS); 5 Adopt and further develop an international climatic research program (ICRP); and 6 Develop an international paleoclimatic data network for the reconstruction of past climates.