Search NASA⌕ Search

SEARCH · Search NASA

Results for “dynamical core”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effect of the Grell-Freitas Deep Convection Scheme in Quasi-Uniform and Variable-Resolution Aquaplanet CAM Simulations

The Grell-Freitas deep convection scheme (GF) has been implemented in the Community Atmosphere Model (CAM) with the nonhydrostatic Model for Prediction Across Scales (MPAS) dynamical core. Aquaplanet simulations have been performed with a global quasi-uniform resolution mesh with ~120 km grid spacing and a variable resolution mesh employing a circular refined region with ~30 km grid spacing centering at the equator and ~120 km elsewhere. GF produces overall patterns of the general circulations comparable to those of other aquaplanet experiments, including those produced using the default Zhang-McFarlane convection scheme (ZM) in CAM Version 5.4. GF alleviates the overestimated occurrence of weak intensity precipitation characteristic of ZM due to GF’s different closure methods and deep-convection trigger conditions. Compared to ZM, the GF characteristics of the equatorial Kelvin waves are closer to observations. GF also better simulates precipitation at variable resolution, with no unrealistic wave distortion by the refinement. Overall, GF shows less sensitivity of convective precipitation and heating/moistening tendencies to changes in horizontal resolution compared to ZM. However, vertically-overlapped total cloud fraction simulated with GF in the tropics is more sensitive to the resolution than ZM due to the larger resolution-sensitivity of high-level cloud fraction in the tropics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

Abstract The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiscale Modeling Framework Using Element‐Based Galerkin Methods for Moist Atmospheric Limited‐Area Simulations

This paper presents a multiscale modeling framework (MMF) to model moist atmospheric limited-area weather. The MMF resolves large-scale convection using a coarse grid while simultaneously resolving local features through numerous fine local grids and coupling them seamlessly. Both large- and small-scale processes are modeled using the compressible Navier-Stokes equations within the Nonhydrostatic Unified Model of the Atmosphere (NUMA), and are discretized using a continuous element-based Galerkin method (spectral elements) with high-order basis functions. Consequently, the large-scale and small-scale models share the same dynamical core but have the flexibility to be adjusted individually. The proposed MMF method is tested in 2D and 3D idealized limited-area weather problems involving storm clouds produced by squall line and supercell simulations. Numerical results from the MMF showed enhanced representation of cloud processes compared to the coarse model.

Kang, Soonpil [Naval Postgraduate School, Monterey↗
DOE OSTI2023

The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System

We present an efficient and performance portable implementation of the Simple Cloud Resolving E3SM Atmosphere Model (SCREAM). SCREAM is a full featured atmospheric global circulation model with a nonhydrostatic dynamical core and state-of-the-art parameterizations for microphysics, moist turbulence and radiation. It has been written from scratch in C++ with the Kokkos library used to abstract the on-node execution model for both CPUs and GPUs. SCREAM is one of only a few global atmosphere models to be ported to GPUs. As far as we know, SCREAM is the first such model to run on both AMD GPUs and NVIDIA GPUs, as well as the first to run on nearly an entire Exascale system (Frontier). On Frontier, we obtained a record setting performance of 1.26 simulated years per day for a realistic cloud resolving simulation.

Taylor, Mark↗
DOE OSTI2025

RDycore/RDycore

A River dynamical core for E3SM

Johnson, Jeffrey N. [Cohere Consulting LLC]↗
DOE OSTI2023

Effects from Time Dependence of Ice Nucleus Activity for Contrasting Cloud Types

Abstract The role of time-dependent freezing of ice nucleating particles (INPs) is evaluated with the “Aerosol–Cloud” (AC) model in 1) deep convection observed over Oklahoma during the Midlatitude Continental Convective Cloud Experiment (MC3E), 2) orographic clouds observed over North California during the Atmospheric Radiation Measurement (ARM) Cloud Aerosol Precipitation Experiment (ACAPEX), and 3) supercooled, stratiform clouds over the United Kingdom, observed during the Aerosol Properties, Processes And Influences on the Earth’s climate (APPRAISE) campaign. AC uses the dynamical core of the WRF Model and has hybrid bin–bulk microphysics and a 3D mesoscale domain. AC is validated against coincident aircraft, ground-based, and satellite observations for all three cases. Filtered concentrations of ice (>0.1–0.2 mm) agree with those observed at all sampled levels. AC predicts the INP activity of various types of aerosol particles with an empirical parameterization (EP), which follows a singular approach (no time dependence). Here, the EP is modified to represent time-dependent INP activity by a purely empirical approach, using our published laboratory observations of time-dependent INP activity. In all simulated clouds, the inclusion of time dependence increases the predicted INP activity of mineral dust particles by 0.5–1 order of magnitude. However, there is little impact on the cloud glaciation because the total ice is mostly (80%–90%) from secondary ice production (SIP) at levels warmer than about −36°C. The Hallett–Mossop process and fragmentation in ice–ice collisions together initiate about 70% of the total ice, whereas fragmentation during both raindrop freezing and sublimation contributes <10%. Overall, total ice concentrations and SIP are unaffected by time-dependent INP activity. In the simulated APPRAISE case, the main causes of persistence of long-lived clouds and precipitation are predicted to be SIP in weak embedded convection and reactivation following recirculation of dust particles in supercooled layer cloud.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nonlinear Response of Atmospheric Blocking to Early Winter Barents–Kara Seas Warming: An Idealized Model Study

Wintertime Ural blocking (UB) has been shown to play an important role in cold extremes over Eurasia, and thus it is useful to investigate the impact of warming over the Barents–Kara Seas (BKS) on the behavior of Ural blocking. In our study the response of UB to stepwise tropospheric warming over the BKS is examined using a dry dynamic core model. Nonlinear responses are found in the frequency and local persistence of UB. The frequency and local persistence of the UB increase with the strength of BKS warming in a less strong range and decrease with the further increase of BKS warming, which is linked to the UB propagation influenced by upstream background atmospheric circulation. For a weak BKS warming, the UB becomes more persistent due to its less westward movement associated with intensified upstream zonal wind and meridional potential vorticity gradient (PVy) in the North Atlantic mid-high latitudes, which corresponds to a negative height response over the North Atlantic high latitudes. When BKS warming is strong, a positive height response appears in the early winter stratosphere, and its subsequent downward propagation leads to a negative NAO response or increased Greenland blocking events, which reduces zonal wind and PVy in the high latitudes from North Atlantic to Europe, thus enhancing the westward propagation of UB and reducing its local persistence. The transition to the negative NAO phase and the retrogression of UB are not found when numerically suppressing the downward influence of weakened stratospheric polar vortex, suggesting a crucial role of the stratospheric pathway in nonlinear responses of UB to the early winter BKS warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Hybrid Climate Modeling System Using AI-assisted Process Emulators

This white paper addresses Focus Area II. We advocate developing a hybrid modeling system to improve the understanding of decadal- and longer-scale predictability of high impact water cycle components. This hybrid model combines a partial differential equation (PDE)-based dynamic core with AI/ML based emulators to represent many of the computationally expensive processes in Earth’s climate models. The hybrid modeling system has the potential to exploit emerging graphics processing unit (GPU)-accelerated architectures and allows for the generation of large ensemble (~1000’s) simulations to better characterize the model uncertainty and understand predictability.

58 GEOSCIENCES↗
DOE OSTI2020

The E3SM version 1 single-column model

Abstract. The single-column model (SCM) functionality of the Energy Exascale Earth System Model version 1 (E3SMv1) is described in this paper. The E3SM SCM was adopted from the SCM used in the Community Atmosphere Model (CAM) but has evolved significantly since then. We describe changes made to the aerosol specification in the SCM, idealizations, and developments made so that the SCM uses the same dynamical core as the full general circulation model (GCM) component. Based on these changes, we describe and demonstrate the seamless capability to “replay” a GCM column using the SCM. We give an overview of the E3SM case library and briefly describe which cases may serve as useful proxies for replicating and investigate some long-standing biases in the full GCM runs while demonstrating that the E3SM SCM is an efficient tool for both model development and evaluation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1

Abstract. This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1∘ horizontal resolution. We show that a factor-of-6 reduction of time step size in all major parts of the model leads to significant changes in the long-term mean climate. Examples of changes in 10-year mean zonal averages include the following: up to 0.5 K of warming in the lower troposphere and cooling in the tropical and subtropical upper troposphere, 1 %–10 % decreases in relative humidity throughout the troposphere, and 10 %–20 % decreases in cloud fraction in the upper troposphere and decreases exceeding 20 % in the subtropical lower troposphere. In terms of the 10-year mean geographical distribution, systematic decreases of 20 %–50 % are seen in total cloud cover and cloud radiative effects in the subtropics. These changes imply that the reduction of temporal truncation errors leads to a notable although unsurprising degradation of agreement between the simulated and observed present-day climate; to regain optimal climate fidelity in the absence of those truncation errors, the model would require retuning. A coarse-grained attribution of the time step sensitivities is carried out by shortening time steps used in various components of EAM or by revising the numerical coupling between some processes. Our analysis leads to the finding that the marked decreases in the subtropical low-cloud fraction and total cloud radiative effect are caused not by the step size used for the collectively subcycled turbulence, shallow convection, and stratiform cloud macrophysics and microphysics parameterizations but rather by the step sizes used outside those subcycles. Further analysis suggests that the coupling frequency between the subcycles and the rest of EAM significantly affects the subtropical marine stratocumulus decks, while deep convection has significant impacts on trade cumulus. The step size of the cloud macrophysics and microphysics subcycle itself appears to have a primary impact on cloud fraction in the upper troposphere and also in the midlatitude near-surface layers. Impacts of step sizes used by the dynamical core and the radiation parameterization appear to be relatively small. These results provide useful clues for future studies aiming at understanding and addressing the root causes of sensitivities to time step sizes and process coupling frequencies in EAM. While this study focuses on EAMv1 and the conclusions are likely model-specific, the presented experimentation strategy has general value for weather and climate model development, as the methodology can help researchers identify and understand sources of time integration error in sophisticated multi-component models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Geometric remapping of particle distributions in the Discrete Element Model for Sea Ice (DEMSI v0.0)

Abstract. A new sea ice dynamical core, the Discrete Element Model for Sea Ice (DEMSI), is under development for use in coupled Earth system models. DEMSI is based on the discrete element method, which models collections of ice floes as interacting Lagrangian particles. In basin-scale sea ice simulations the Lagrangian motion results in significant convergence and ridging, which requires periodic remapping of sea ice variables from a deformed particle configuration back to an undeformed initial distribution. At the resolution required for Earth system models we cannot resolve individual sea ice floes, so we adopt the sub-grid-scale thickness distribution used in continuum sea ice models. This choice leads to a series of hierarchical tracers depending on ice fractional area or concentration that must be remapped consistently. The circular discrete elements employed in DEMSI help improve the computational efficiency at the cost of increased complexity in the effective element area definitions for sea ice cover that are required for the accurate enforcement of conservation. An additional challenge is the accurate remapping of element values along the ice edge, the location of which varies due to the Lagrangian motion of the particles. In this paper we describe a particle-to-particle remapping approach based on well-established geometric remapping ideas that enforces conservation, bounds preservation, and compatibility between associated tracer quantities, while also robustly managing remapping at the ice edge. One element of the remapping algorithm is a novel optimization-based flux correction that enforces concentration bounds in the case of nonuniform motion. We demonstrate the accuracy and utility of the algorithm in a series of numerical test cases.

58 GEOSCIENCES↗
DOE OSTI2023

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

58 GEOSCIENCES↗
DOE OSTI2024

HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model

Abstract. 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.

Hollowed, Joseph P. (ORCID:0000000286581672)↗
DOE OSTI2025

A Fortran–Python interface for integrating machine learning parameterization into earth system models

Abstract. Parameterizations in earth system models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation, and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran–Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and scikit-learn. We demonstrate the interface's modularity and reusability through two cases: an ML trigger function for convection parameterization and an ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Fortran-Python Interface for Integrating Machine Learning Parameterization into Earth System Models

Parameterizations in Earth System Models (ESMs) are subject to biases and uncertainties arising from subjective empirical assumptions and incomplete understanding of the underlying physical processes. Recently, the growing representational capability of machine learning (ML) in solving complex problems has spawned immense interests in climate science applications. Specifically, ML-based parameterizations have been developed to represent convection, radiation and microphysics processes in ESMs by learning from observations or high-resolution simulations, which have the potential to improve the accuracies and alleviate the uncertainties. Previous works have developed some surrogate models for these processes using ML. These surrogate models need to be coupled with the dynamical core of ESMs to investigate the effectiveness and their performance in a coupled system. In this study, we present a novel Fortran-Python interface designed to seamlessly integrate ML parameterizations into ESMs. This interface showcases high versatility by supporting popular ML frameworks like PyTorch, TensorFlow, and Scikit-learn. We demonstrate the interface's modularity and reusability through two cases: a ML trigger function for convection parameterization and a ML wildfire model. We conduct a comprehensive evaluation of memory usage and computational overhead resulting from the integration of Python codes into the Fortran ESMs. By leveraging this flexible interface, ML parameterizations can be effectively developed, tested, and integrated into ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Alcator C-Mod: Research in support of ITER and steps beyond

This paper presents an overview of recent highlights from research on Alcator C-Mod. Significant progress has been made across all research areas over the last two years, with particular emphasis on divertor physics and power handling, plasma-material-interaction studies, ELM-suppressed pedestal dynamics, core transport and turbulence, and RF heating and current drive utilizing Ion Cyclotron and Lower Hybrid tools. Specific results of particular relevance to ITER include: inner wall SOL transport studies that have led, together with results from other experiments, to the change of the detailed shape of the inner wall in ITER; runaway electron studies showing that the critical electric field required for runaway generation is much higher than predicted from collisional theory; core tungsten impurity transport studies reveal that tungsten accumulation is naturally avoided in typical C-Mod conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Impact of atomic substitution on core-hole relaxation dynamics: A study of Br 2 and IBr

Understanding inner-shell decay processes in heavy-element molecules is essential for unraveling x-ray-induced photodynamics and advancing molecular imaging techniques. Here, in this study, we investigate the influence of atomic substitution on core-hole relaxation dynamics and molecular fragmentation in Br 2 and IBr, initiated by x-ray ionization absorption at the Br K-edge. Using a combination of x-ray/ion coincidence measurements and Monte Carlo/molecular dynamics simulations, we track the charge distribution and the kinetic energy release (KER) of fragment ions with a total charge from 2+ to 8+. For both molecules, the simulated KER values show good agreement with experiment across different fragmentation channels. Our comparison reveals that substituting Br with the heavier I atom in IBr has a minimal impact on the inner-shell electronic decay process but significantly influences nuclear motion, leading to slower dissociation and thus a KER close to the Coulomb limit—an effect attributed to the atomic mass. These findings highlight the interplay between electronic and nuclear effects in molecular fragmentation, particularly in heavy-element species, and provide new insights into medical therapies, structural biology, and astrophysics.

Bhat, Nivedita [Argonne National Laboratory (ANL),↗
DOE OSTI2025

Time-series RNA metabarcoding of the active Populus tremuloides root microbiome reveals hidden temporal dynamics and dormant core members

The rhizosphere is a critical interface between plant roots and soil, harboring diverse microbial communities that are essential to plant and ecosystem health. Although these communities exhibit stark temporal dynamics, their dormancy/activity transitions remain poorly understood. Such transitions may enable microbes to rapidly adjust functional contributions faster than community turnover alone would allow. Here, we used RNA metabarcoding to characterize the active fraction of microbial communities on the roots of quaking aspen (Populus tremuloides) in a time-series study across a natural environmental gradient. We explore cryptic temporal microbial community dynamics of rhizosphere communities at the ecosystem scale. The active rhizosphere bacterial and fungal communities were more temporally dynamic than total communities, while total communities exhibited a stronger response to site-specific conditions. Notably, some core microbiome members were often inactive, yielding a smaller “active core” subset. The fungal endophyte Hyaloscypha finlandica was the only microbe that was both present and active in all plots across all timepoints. Soil temperature strongly influenced both total and active community composition, with the fungal class Eurotiomycetes showing a temperature-dependent seasonal decline in abundance. Together, these results reveal that modulation of microbial activity levels is a key mechanism by which the plant root holobiont responds to environmental variation, and that even dominant symbionts may frequently persist in dormancy within the rhizosphere.

Community Structure and Diversity↗