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At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impacts of Resolution on Heavy‐Precipitating Storms in Climate Model Hindcasts

The present study investigates the impact of horizontal resolutions on heavy‐precipitating storms using the Energy Exascale Earth System Model version2 (E3SMv2) at low (∼100 km, LR) and high (∼25 km, HR) resolutions through short‐range hindcasts. The short‐range hindcast approach ensures a faithful comparison of model resolution in simulating the same storm events under a controlled large‐scale environment. Using a phenomenon‐based framework, we attribute precipitation to specific storm types: tropical cyclones (TCs), extratropical cyclones, atmospheric rivers, and mesoscale convective systems (MCSs). Our findings show that E3SM hindcasts with both HR and LR configurations significantly underestimate storm‐associated precipitation intensity but overestimate precipitation from other sources. Furthermore, both HR and LR hindcasts face significant challenges in accurately simulating extreme precipitation events, particularly over MCS hotspots. Nevertheless, HR simulations capture more detailed and intense precipitation patterns with an improved representation of storm dynamics. HR hindcasts produce 16% more storm precipitation compared to LR. For precipitation extremes, HR simulates a 33% higher 99th percentile precipitation magnitudes compared to LR, and most of the increment comes from these four heavy‐precipitating storm types. The increase in precipitation mainly comes from stratiform precipitation rather than convective precipitation. The improvement in HR simulations varies across different storm types with TC showing the largest improvement. The phenomenon‐based approach provides important insights into precipitation simulations especially for extremes. Our results emphasize the need for further refinement in high‐resolution models to improve the accuracy of precipitation predictions, which is crucial for better understanding and mitigating climate change impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Porting a fast-running wildfire plume model for use in national security and climate applications

In this project, the code for the fast-running Freitas wildfire plume model was successfully ported into the NARAC system, creating an operations-ready capability for predicting the transport of hazardous smoke from large wildfires. “Porting” refers to copying a section of code from one software package to another while accounting for unique dependencies in each software package. In this case, the code was ported from the Weather Research and Forecasting (WRF) Model, which is used extensively by the project team. To verify correct implementation, model results from the updated NARAC system were compared to those from WRF and the High-Resolution Rapid Refresh (HRRR) forecast model, which also employs the Freitas scheme. The NARAC User Interface (UI) and code documentation were also updated to include the new wildfire plume option, and a wildfire case was added to the NARAC test suite. The code was used in a real-time, semi-operational setup to predict smoke transport during the Cerro Pelado fire near Los Alamos National Laboratory in May 2022. The Freitas plume model was also tested for LLNL climate applications. Although the code was not ported into the Energy Exascale Earth System Model (E3SM), it was tested offline using E3SM atmospheric inputs, and results were compared to existing high-resolution plume predictions from large-eddy simulations in WRF. The code implementation and testing completed during this project will enable future work predicting smoke transport from large wildfires in LLNL national security and climate mission spaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Warm-season net CO 2 uptake outweighs cold-season emissions over Alaskan North Slope tundra under current and RCP8.5 climate

Abstract Arctic warming has increased vegetation growth and soil respiration during recent decades. The rate of Arctic warming will likely amplify over the 21st century. Previous studies have revealed that the most severe Arctic warming occurred during the cold season (September to May). The cold-season warming has posited significant CO 2 emissions to the atmosphere via respiration, possibly offsetting warm-season (June to August) net CO 2 uptake. However, prevailing Earth system land models poorly represent cold-season CO 2 emissions, making estimates of Arctic tundra annual CO 2 budgets highly uncertain. Here, we demonstrate that an improved version of the energy exascale Earth system model (E3SM) land model (ELMv1-ECA) captures the large amount of cold-season CO 2 emissions over Alaskan Arctic tundra as reported by two independent, observationally-constrained datasets. We found that the recent seven-decades warming trend of cold-season soil temperature is three times that of the warm-season. The climate sensitivity of warm-season net CO 2 uptake, however, is threefold higher than for the cold-season net CO 2 loss, mainly due to stronger plant resilience than microbial resilience to hydroclimatic extremes. Consequently, the modeled warm-season net CO 2 uptake has a larger positive trend (0.74 ± 0.14 gC m −2 yr −1 ) than that of cold-season CO 2 emissions (0.64 ± 0.11 gC m −2 yr −1 ) from 1950 to 2017, supported by enhanced plant nutrient uptake and increased light- and water-use efficiency. With continued warming and elevated CO 2 concentrations under the representative concentration pathway (RCP) 8.5 scenario, the increasing rate of warm-season net CO 2 uptake is more than twice the rate of cold-season emissions (1.33 ± 0.32 gC m −2 yr −1 vs 0.50 ± 0.12 gC m −2 yr −1 ), making the modeled Alaskan Arctic tundra ecosystem a net CO 2 sink by 2100. However, other geomorphological and ecological disturbances (e.g. abrupt permafrost thaw, thermokarst development, landscape-scale hydrological changes, wildfire, and insects) that are not considered here might alter our conclusion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Numerical simulation of compressible fluid-dynamics in the chamber of inertial fusion energy systems

Here, this paper aims to establish new and innovative modeling capabilities for analyzing chambers in Inertial Fusion Energy (IFE) systems. IFE is emerging as a promising method to achieve fusion power production, but several challenges must be overcome to develop an IFE pilot plant or deploy commercial IFE systems. These challenges are both theoretical and technical, encompassing a deeper understanding of the underlying physical phenomena and the development of new technologies and materials. One of the needs is to develop mathematical models to describe IFE systems and numerical tools to simulate them. This paper contributes to this endeavor by presenting a new OpenFOAM solver for IFE systems, focusing on gas dynamics in their chambers. The analysis and development of chamber designs will play a significant role in the transition from single-shot experiments to high-repetition rates, as there is a need to protect the chamber walls from the intense radiation fields produced by fusion reactions. A promising design option, normally referred to as thick wall chamber design, consists in using lithium or molten salt jet arrays within the chamber. A critical phenomenon is the venting of high-pressure gases from the center to the external part of the chamber, passing through the blanket array. This process involves the propagation and attenuation of strong pressure waves, requiring suitable modeling approaches for compressible fluid-dynamics. The solver proposed in this work implements a multi-material hydrodynamics model tailored to accurately describe the non-linear propagation of pressure waves while avoiding numerical oscillation issues typical of high-velocity compressible simulation. This solver is verified against numerical test cases, validated against experimental data, and applied to the analysis of the High-Yield Lithium-Injection Fusion-Energy (HYLIFE-I) concept. The relevance of this paper is threefold. Firstly, it contributes to developing and testing modeling approaches for compressible fluid-dynamics phenomena, with specific focus on the new and unexplored topic of IFE thick-liquid-wall blanket modeling. Secondly, it marks one of the first applications of the OpenFOAM library in the research field of IFE systems. Finally, the investigated problem is of practical interest for IFE developers, as it provides useful indications about relevant phenomena in pressure wave propagation in the chamber of these systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Cyber-Physical Dynamic System (CPDS) Modeling for Frequency Regulation and AGC Services of Distributed Energy Resources

The substantial integration of renewable energy brings significant challenges to balance the system in real time because of the variability and intermittency of renewable power. For the reliable system operation, the frequency regulation service is used to stabilize the system frequency through automatically balancing the system generation and load. On one hand, with the substantially increasing deployment of renewable energy in electricity system, the requirement of frequency regulation (FR) services increases significantly. On the other hand, the current main resource of FR services, the controllable conventional generation, is continuously decreasing in the system generation mix. This means that in the future high renewable penetration power system, additional and alternative reliable FR services providers such as distributed energy storage (DES) resources should be explored. Although the capability of utility-scale energy storage to provide FR services has been demonstrated, the integrated control and dynamic modeling of distributed energy resource (DER) providing frequency regulation grid services has been rarely explored. There are several challenges to adopt DERs to provide reliable grid services as illustrated in FERC 755. First, the distributed installation of DES requires a comprehensive cyber-physical dynamic system (CPDS) modeling to fully consider the impacts of the communication latency variability on its real time FR provision capability. Unlike the conventional generators, there are two-layer communication between DERs and system operators which increase the communication delay. Second, unlike the conventional generators whose dynamic models have been comprehensively studied, the difference among individual DER components' power-dynamic characteristic brings challenges in the accurate dynamic AGC modeling of its power-frequency relationship. Third, the temporal dependent state of charge uncertainty of DERs challenges DERs' power and frequency regulation capacities scheduling in the look-ahead generation scheduling. Therefore, the aggregator should optimize the frequency regulation provision from individual DER in real time. To overcome these challenges, this project proposes a cyber-physical dynamic system (CPDS) model to handle the uncertainty of DERs two-layer communication latency and power dynamics. The variability of DERs' communication delay and dynamic constraints will be comprehensively modeled. The DERs' AGC model with communication delay is designed to validate DESs' frequency regulation services. Like current performance-based frequency regulation evaluation, the delivery of DERs' frequency regulation will be assessed through post-analysis of the actual AGC response with respect to the AGC control signal from system operators. Consequently, the reliability improvement with DERs providing reliable frequency regulation services can be evaluated from a comprehensive perspective considering all the dynamics of communication and power.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Barotropic tides in MPAS-Ocean (E3SM V2): impact of ice shelf cavities

Abstract. Oceanic tides are seldom represented in Earth system models (ESMs) owing to the need for high horizontal resolution to accurately represent the associated barotropic waves close to coasts. This paper presents results of tides implemented in the Model for Prediction Across Scales–Ocean or MPAS-Ocean, which is the ocean component within the U.S. Department of Energy developed Energy Exascale Earth System Model (E3SM). MPAS-Ocean circumvents the limitation of low resolution using unstructured global meshing. We are at this stage simulating the largest semidiurnal (M2, S2, N2) and diurnal (K1, O1) tidal constituents in a single-layer version of MPAS-O. First, we show that the tidal constituents calculated using MPAS-Ocean closely agree with the results of the global tidal prediction model TPXO8 when suitably tuned topographic wave drag and bottom drag coefficients are employed. Thereafter, we present the sensitivity of global tidal evolution due to the presence of Antarctic ice shelf cavities. The effect of ice shelves on the amplitude and phase of tidal constituents are presented. Lower values of complex errors (with respect to TPXO8 results) for the M2 tidal constituents are observed when the ice shelf is added in the simulations, with particularly strong improvement in the Southern Ocean. Our work points towards future research with varying Antarctic ice shelf geometries and sea ice coupling that might lead to better comparison and prediction of tides and thus better prediction of sea-level rise and also the future climate variability.

58 GEOSCIENCES↗
DOE OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods

We report home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment. This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers

Flooding events pose significant risk to human life, property, and infrastructure. Physically-consistent quantification of altered flood risks in global models requires hyper-resolution (~1 km) or fine flood simulations using two-dimensional (2D) physics schemes, both of which are unavailable in the current generation Earth System Models. Here, in this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation (SWE) library for the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries that allows it to run efficiently on CPUs and GPUs, as well as select a time-integration algorithm at runtime without requiring any code modifications. RDycore achieves spatial error convergence rates for problems with analytical and manufactured solutions similar to those reported previously in the literature, or consistent with the implemented first-order spatial discretization scheme. RDycore's accuracy in predicting flooding for a well-studied dam break problem is comparable to existing SWE models. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs when using 320 compute nodes on DOE's Perlmutter and Frontier supercomputers, respectively. The one-way coupling of the RDycore library within E3SM is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The E3SM--RDycore simulations at 30 m spatial resolution accurately simulate maximum water height during the hurricane when benchmarked against a previously published study and achieve a speedup of 15x (Perlmutter) and 21x (Frontier) on GPUs relative to CPUs. The work presented here is the foundational step in providing hardware and algorithmic portability framework for simulating kilometer-scale river dynamics within E3SM.

Flood Simulation↗
DOE OSTI2022

Global Dust Cycle and Direct Radiative Effect in E3SM Version 1: Impact of Increasing Model Resolution

Quantification of dust aerosols in Earth System Models (ESMs) has important implications for water cycle and biogeochemistry studies. This study examines the global life cycle and direct radiative effects (DREs) of dust in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1), and the impact of increasing model resolution both horizontally and vertically. The default 1° E3SMv1 captures the spatial and temporal variability in the observed dust aerosol optical depth (DAOD) reasonably well, but overpredicts dust absorption in the shortwave (SW). Simulations underestimate the dust vertical and long-range transport, compared with the satellite dust extinction profiles. After updating dust refractive indices and correcting for a bias in partitioning size-segregated emissions, both SW cooling and longwave (LW) warming of dust simulated by E3SMv1 are increased and agree better with other recent studies. The estimated net dust DRE of –0.42 Wm –2 represents a stronger cooling effect than the observationally based estimate –0.2 Wm –2 (–0.48 to +0.2), due to a smaller LW warming. Constrained by a global mean DAOD, model sensitivity studies of increasing horizontal and vertical resolution show strong influences on the simulated global dust burden and lifetime primarily through the change of dust dry deposition rate; there are also remarkable differences in simulated spatial distributions of DAOD, DRE, and deposition fluxes. Thus, constraining the global DAOD is insufficient for accurate representation of dust climate effects, especially in transitioning to higher- or variable-resolution ESMs. Better observational constraints of dust vertical profiles, dry deposition, size, and LW properties are needed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Global Dust Cycle and Direct Radiative Effect in E3SM Version 1: Impact of Increasing Model Resolution

Quantification of dust aerosols in Earth System Models (ESMs) has important implications for water cycle and biogeochemistry studies. This study examines the global life cycle and direct radiative effects (DREs) of dust in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1), and the impact of increasing model resolution both horizontally and vertically. The default 1° E3SMv1 captures the spatial and temporal variability in the observed dust aerosol optical depth (DAOD) reasonably well, but overpredicts dust absorption in the shortwave (SW). Simulations underestimate the dust vertical and long-range transport, compared with the satellite dust extinction profiles. After updating dust refractive indices and correcting for a bias in partitioning size-segregated emissions, both SW cooling and longwave (LW) warming of dust simulated by E3SMv1 are increased and agree better with other recent studies. The estimated net dust DRE of −0.42 Wm−2 represents a stronger cooling effect than the observationally based estimate −0.2 Wm−2 (−0.48 to +0.2), due to a smaller LW warming. Constrained by a global mean DAOD, model sensitivity studies of increasing horizontal and vertical resolution show strong influences on the simulated global dust burden and lifetime primarily through the change of dust dry deposition rate; there are also remarkable differences in simulated spatial distributions of DAOD, DRE, and deposition fluxes. Thus, constraining the global DAOD is insufficient for accurate representation of dust climate effects, especially in transitioning to higher- or variable-resolution ESMs. Better observational constraints of dust vertical profiles, dry deposition, size, and LW properties are needed.

dust aerosol↗
DOE OSTI2024

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗
DOE OSTI2024

Integrating NDVI-Based Within-Wetland Vegetation Classification in a Land Surface Model Improves Methane Emission Estimations

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model Coupling Through Learned Representations

Reliable climate predictions are important for making robust decisions in response to the changing climate. This project aims to reduce mis-modeling uncertainties arising from the representation of the land-atmosphere coupling in the Energy Exascale Earth System Model (E3SM) by using a machine learning approach. This approach will use an encoder-decoder architecture to represent the information that is developed in the land model and given to the atmosphere model. The simulated data will be taken from the E3SM simulation. However, the incorporation of observed data into the simulated dataset reduces mis-modeling uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SCREAM Beijing Flood RRM Technical Note

The Beijing Convection-Permitting (CP) Regionally Refined Model (RRM) used for 2023 Jingjinji Flood event is developed based on the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0 (Fortran code) under the United States (U.S) Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) project (Caldwell et al., 2021) and the regionally refined model (RRM) configuration (Tang et al., 2019).

58 GEOSCIENCES↗
DOE OSTI2026

EAMxx 3.25 km Western Pacific RRM Technical Note

The Western Pacific Convection-Permitting (CP) Regionally Refined Model (RRM) used for 2023 Jingjinji Flood event is developed based on the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0 (Fortran code) under the United States (U.S) Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) project (Caldwell et al., 2021) and the regionally refined model (RRM) configuration (Tang et al., 2019, 2023; Zhang et al., 2024; Bogenschutz et al., 2024).

58 GEOSCIENCES↗
DOE OSTI2022

MPAS-Seaice (v1.0.0): sea-ice dynamics on unstructured Voronoi meshes

Abstract. We present MPAS-Seaice, a sea-ice model which uses the Model for Prediction Across Scales (MPAS) framework and spherical centroidal Voronoi tessellation (SCVT) unstructured meshes. As well as SCVT meshes, MPAS-Seaice can run on the traditional quadrilateral grids used by sea-ice models such as CICE. The MPAS-Seaice velocity solver uses the elastic–viscous–plastic (EVP) rheology and the variational discretization of the internal stress divergence operator used by CICE, but adapted for the polygonal cells of MPAS meshes, or alternatively an integral (“finite-volume”) formulation of the stress divergence operator. An incremental remapping advection scheme is used for mass and tracer transport. We validate these formulations with idealized test cases, both planar and on the sphere. The variational scheme displays lower errors than the finite-volume formulation for the strain rate operator but higher errors for the stress divergence operator. The variational stress divergence operator displays increased errors around the pentagonal cells of a quasi-uniform mesh, which is ameliorated with an alternate formulation for the operator. MPAS-Seaice shares the sophisticated column physics and biogeochemistry of CICE and when used with quadrilateral meshes can reproduce the results of CICE. We have used global simulations with realistic forcing to validate MPAS-Seaice against similar simulations with CICE and against observations. We find very similar results compared to CICE, with differences explained by minor differences in implementation such as with interpolation between the primary and dual meshes at coastlines. We have assessed the computational performance of the model, which, because it is unstructured, runs with 70 % of the throughput of CICE for a comparison quadrilateral simulation. The SCVT meshes used by MPAS-Seaice allow removal of equatorial model cells and flexibility in domain decomposition, improving model performance. MPAS-Seaice is the current sea-ice component of the Energy Exascale Earth System Model (E3SM).

58 GEOSCIENCES↗
DOE OSTI2021

Accelerating Clean Energy @ Scale

NREL helps translate community energy ambitions into actions. Rooted in inclusive community engagement, Accelerating Clean Energy @ Scale (ACES) brings together NREL’s experience, expertise, and capabilities to illuminate pathways for clean, affordable, equitable, secure, and resilient energy systems. Through customized, holistic modeling and analysis, we uncover insights and collaboratively develop strategies that address communities’ unique energy- system goals and diverse stakeholder priorities. NREL provides support in evaluating and implementing community-selected options, as well as assistance for workforce development.

accelerating clean energy scale analysis↗