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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.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Late Paleozoic Evolution of the Anadarko Basin: Implications for Laurentian Tectonics and the Assembly of Pangea

Abstract Interplay between marginal and intraplate late Paleozoic tectonics in southeastern Laurentia is a subject of debate. In southern Laurentia, intraplate deformation has been attributed to combinations of compressive forces from orogenic belts in the west, southwest, and southeast. While left‐lateral transpression is generally an accepted mechanism for formation of uplifts in the Southern Oklahoma Aulacogen (SOA), to date no studies have explained the driving mechanisms of these kinematics. Here, we analyze the Southern Oklahoma Transpressional System (SOTS), a ∼500 km long ∼50 km wide fault zone that extends from the southeastern Laurentian margin into the plate interior along Cambrian lithospheric weaknesses inherited from the SOA. We use a compilation of published studies to present detailed, time‐integrated sedimentary thickness maps of SOTS‐related basins. We combine these maps with a new interpretation of three 2D seismic reflection lines to constrain timing and kinematics of all major late Paleozoic SOTS fault zones. We show that the SOTS kinematics evolved from contraction to left‐lateral strike‐slip during Mississippian‐Pennsylvanian time. Tectonic activity and fault kinematics in the SOTS are explained by a model in which a remnant ocean basin closed diachronously from northeast to southwest along the Southern Appalachian and Ouachita‐Marathon orogens. Left‐lateral transpression during diachronous closure of the Rheic oceanic basin along the Ouachita‐Marathon margin is related to rotation of the stress field due to changes in slab‐pull and eastward drift of Laurentia toward Gondwana. We speculate that the SOTS acted in part as a STEP (subduction‐transfer edge propagator) fault that followed the pre‐existing SOA.

Hobbs, Noah. F.↗
DOE OSTI2022

Structural and Electronic Transitions in Liquid FeO Under High Pressure

Abstract FeO represents an important end‐member for planetary interiors mineralogy. However, its properties in the liquid state under high pressure are poorly constrained. Here, in situ high‐pressure and high‐temperature X‐ray diffraction experiments, ab initio simulations, and thermodynamic calculations are combined to study the local structure and density evolution of liquid FeO under extreme conditions. Our results highlight a strong shortening of the Fe‐Fe distance, particularly pronounced between ambient pressure and ∼40 GPa, possibly related with the insulator to metal transition occurring in solid FeO over a similar pressure range. Liquid density is smoothly evolving between 60 and 150 GPa from values calculated for magnetic liquid to those calculated for non‐magnetic liquid, compatibly with a continuous spin crossover in liquid FeO. The present findings support the potential decorrelation between insulator/metal transition and the high‐spin to low‐spin continuous transition, and relate the changes in the microscopic structure with macroscopic properties, such as the closure of the Fe‐FeO miscibility gap. Finally, these results are used to construct a parameterized thermal equation of state for liquid FeO providing densities up to pressure and temperature conditions expected at the Earth's core‐mantle boundary.

Morard, Guillaume↗
DOE OSTI2022

Aircraft Observations of Turbulence in Cloudy and Cloud-Free Boundary Layers Over the Western North Atlantic Ocean From ACTIVATE and Implications for the Earth System Model Evaluation and Development

This study examines boundary layer turbulence derived from high temporal resolution meteorological measurements from 40 research flights over the western North Atlantic Ocean during the 2020 deployments of ACTIVATE. Frequency distributions of various turbulent quantities reveal stronger turbulence during the winter deployment than in summer and for cloud‐topped than in cloud‐free boundary layers during the summer deployment. Maximum turbulence kinetic energy (TKE) is most often within cloud from observations in winter and summer, whereas it is mostly below cloud in both seasons by a global model turbulence parameterization. Further, bivariate frequency distributions are consistent with the bivariate Gaussian probability distribution functions assumed for the closure of higher‐order turbulence/shallow convection parameterizations used by some global models. Turbulence simulated by the Community Atmosphere Model version 6 and the Energy Exascale Earth System Model Atmosphere Model version 2 using such parameterizations is not as strong as observed, with more TKE going into vertical wind perturbations rather than into zonal wind perturbations as observed, suggesting that the treatment of turbulence in Earth system models still needs to be further improved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Long‐Term Observations of Turbulence Vertical Velocity Spectra in a Convective Mixed Layer: Dependence on Land‐Surface Forcing in the U.S. Southern Great Plains

Doppler lidar vertical velocity retrievals were analyzed for the scale and structure of mixed-layer turbulence over a 7-year period, on fair-weather warm season days (442 cases) in the U.S. Southern Great Plains. Based on the hypothesized influence of land-surface forcing and updraft size on convective boundary layer clouds, a spectral analysis was performed to quantify effects of surface forcing (surface buoyancy flux, friction velocity, and evaporative fraction) on turbulence scale. Significant (order-of-magnitude) variations in spectral density were found in the energy-production subrange and mesoscale regimes. Integral scale decreased with increasing buoyancy flux (and Monin-Obukhov stability parameter), while spectral density in the energy-production subrange increased, implying a transition to buoyancy-driven cellular structures with narrower updrafts. The influence of stability parameter was limited to the neutral to convective transition, and could not explain the wide variation in spectral density in the mesoscale regime. However, high friction velocity (u* > 0.5 m s –1 ) was associated with larger integral scale (updraft width), and a greater portion of spectral density in the mesoscale regime. Lidar profiles and radar reflectivity for individual cases revealed evidence of roll and wave-like structures contributing to mesoscale variability on high friction velocity days, suggesting shear instabilities on days with wetter land surface conditions and smaller buoyancy flux. The role of friction velocity emphasizes the multivariate nature of surface influences on turbulence, and implies that variance-based turbulence closures may not be adequate for capturing effects of surface forcing on updraft size.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantification of Physical and Numerical Mixing in a Coastal Ocean Model Using Salinity Variance Budgets

Numerical mixing, the spurious mixing primarily generated by the discretization of advection, is often significant in estuarine and coastal models due to sharp, energetic fronts. In this study, we compare on- and offline estimates of numerical mixing in a submesoscale-resolving realistic simulation of the ocean state over the Texas-Louisiana continental shelf. While offline estimates of numerical mixing differ from online estimates, offline methods may be the only analysis available. This study offers insight into the differences between the on- and offline methods. We use two methods to estimate numerical mixing offline, based on salinity squared $s^2$ and volume-mean salinity variance $s^{\prime^2}$. Numerical mixing estimated from the $s^{\prime^2}$ budget is generally within 60\% of the magnitude for the online method but captures the temporal variability well. However, the $s^2$ budget compares poorly due to larger truncation errors associated with the tendency and advection terms, which can be reduced by increasing the model output frequency. We also investigate the effects of horizontal resolution on numerical mixing using a two-way nested grid. The volume-integrated numerical mixing constitutes 57\% of the bulk physical mixing -- the mixing prescribed by the turbulence closure scheme -- in the coarse model and may exceed the physical mixing by half an order of magnitude. We find that numerical mixing is reduced by 35\% on average in the nested model, less than expected based on scaling of the numerical mixing for an upwind advection scheme, likely due to new dynamical processes that emerge in the nested simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length

Abstract Stomatal optimization theory is a commonly used framework for modeling how plants regulate transpiration in response to the environment. Most stomatal optimization models assume that plants instantaneously optimize a reward function such as carbon gain. However, plants are expected to optimize over longer timescales given the rapid environmental variability they encounter. There are currently no observational constraints on these timescales. Here, a new stomatal model is developed and is used to analyze the timescales over which stomatal closure is optimized. The proposed model assumes plants maximize carbon gain subject to the constraint that they cannot draw down soil moisture below a critical value. The reward is integrated over time, after being weighted by a discount factor that represents the timescale ( τ ) that a plant considers when optimizing stomatal conductance to save water. The model is simple enough to be analytically solvable, which allows the value of τ to be inferred from observations of stomatal behavior under known environmental conditions. The model is fitted to eddy covariance data in a range of ecosystems, finding the value of τ that best predicts the dynamics of evapotranspiration at each site. Across 82 sites, the climate metrics with the strongest correlation to τ are measures of the average number of dry days between rainfall events. Values of τ are similar in magnitude to the longest such dry period encountered in an average year. The results here shed light on which climate characteristics shape spatial variations in ecosystem‐level water use strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Numerical Mixing Suppresses Submesoscale Baroclinic Instabilities Over Sloping Bathymetry

Abstract The impacts of spurious numerical salinity mixing on the larger‐scale flow and tracer fields are characterized using idealized simulations. The idealized model is motivated by realistic simulations of the Texas‐Louisiana shelf and features oscillatory near‐inertial wind forcing. can exceed the physical mixing from the turbulence closure in frontal zones and within the mixed layer. This suggests that simulated mixing processes in frontal zones are driven largely by . Near‐inertial alongshore wind stress amplitude is varied to identify a base case that maximizes the ratio of to in simulations with no prescribed horizontal mixing. We then test the sensitivity of the base case with three tracer advection schemes (MPDATA, U3HC4, and HSIMT) and conduct ensemble runs with perturbed bathymetry. Instability growth is evaluated using the volume‐integrated eddy kinetic energy and available potential energy . While all schemes have similar total mixing, the HSIMT simulations have over double the volume‐integrated and 20% less relative to other schemes, which suppresses the release of and reduces the by roughly 25%. This results in reduced isohaline variability and steeper isopycnals, evidence that enhanced suppresses instability growth. Differences in and between the MPDATA and U3HC4 simulations are marginal. However, the U3HC4 simulations have 25% more . Experiments with variable horizontal viscosity and diffusivity coefficients show that small amounts of prescribed horizontal mixing improve the representation of the ocean state for all advection schemes by reducing the and increasing the .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

JAX-CanVeg: A Differentiable Land Surface Model

Land surface models consider the exchange of water, energy, and carbon along the soil-canopy-atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process-based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process-based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX-CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX-CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball-Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade-off when learning observations of both LE and NEE, and (c) benefiting a multi-layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade-off over the process-based model. Additionally, the multi-layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land-atmosphere interactions by leveraging the benefits of both data-driven learning and process-based modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Process‐Oriented Calibration of a Turbulence Scheme in the DOE's Global Storm‐Resolving Model Using Machine Learning

A process‐oriented calibration framework is developed for the Simplified Higher‐Order Closure (SHOC) turbulence scheme in DOE's Simple Cloud Resolving E3SM Atmospheric Model (SCREAM). This framework leverages machine learning surrogates and observational constraints to efficiently calibrate SHOC adjustable parameters across two convective regimes: clear‐sky dry convective boundary layer and fair‐weather shallow cumulus clouds from ARM observations. We use perturbed‐parameter ensembles of a doubly periodic version of SCREAM to train surrogates and apply Markov Chain Monte Carlo sampling guided by cost functions based on benchmarking large‐eddy simulations and observations to identify optimized parameter sets that perform well in both regimes. The calibrated SHOC parameters substantially improve boundary‐layer turbulence and cloud boundaries, and modeled cloud fraction and radiative effects align better with observations than the default. These results demonstrate that combining multiple process‐specific convective regimes with machine‐learning surrogates can reduce parametric uncertainties and yield a model more faithful to cloud–turbulence interactions.

58 GEOSCIENCES↗
DOE OSTI2025

Evaluating E3SM Global Storm‐Resolving Model Simulations of Deep Convection: Insights From DP‐SCREAM During TRACER

Global Storm-Resolving Models (GSRMs) are becoming increasingly vital for advancing climate modeling and improving the prediction of extreme weather events. Houston, a coastal region frequently affected by deep convective storms, offers an ideal setting to evaluate the ability of GSRMs to simulate deep convection. This study assesses the performance of the Doubly Periodic Simple Cloud-Resolving E3SM (Energy Exascale Earth System Model) Atmosphere Model (DP-SCREAM) using observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. DP-SCREAM effectively reproduces the diurnal cycles of clouds and precipitation, demonstrating much greater skill than the E3SM single column model. The DP-SCREAM is demonstrated to be applicable to coastal regions, partially due to the forcing data sets already capturing the influence of breezes. DP-SCREAM also replicates biases persistent in the global version of SCREAM: the underrepresentation of boundary layer shallow clouds, a lack of mid-level congestus clouds, and the popcorn convection, characterized by small and disorganized convective cells generating the strongest precipitation. To investigate these issues, two sensitivity experiments were conducted: increasing the mixing length and scaling up the buoyancy flux within the Simplified Higher Order Closure scheme. Increasing the mixing length improved mid-level congestus representation and reduced unrealistic early morning fog occurrence. Enhancing buoyancy flux only marginally improved the bias of underproduced big convective cells. In conclusion, an additional resolution sensitivity test at 0.5 km grid spacing demonstrated that a refined horizontal resolution alone is insufficient to resolve these biases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

An Optimized Parameterization of Sub‐Grid Scale Advection for Convection Permitting Models

Convection‐permitting models (CPMs) explicitly resolve deep convection yet under‐resolve the organized lateral exchanges among drafts and their environment that control entrainment/detrainment, precipitation efficiency, and mesoscale structure. In this work, we introduce the Optimized Advection Scheme (OAS), which introduces a small rotation of the Cartesian frame of reference for the horizontal winds relative to other variables used in advection that induces cross‐gradient transport to mimic under‐resolved convective mixing. The rotation angle is selected to minimize the Kullback–Leibler divergence between the simulated and satellite observed precipitation intensity distributions, yielding a physically consistent perturbation that is computationally inexpensive and portable. Optimized Advection Scheme is implemented in WRF and evaluated over Amazon (April 2014). It shifts precipitation–precipitable‐water joint distributions toward lighter rain, reduces overly intense rates, and improves mesoscale convective system (MCS) lifetime and propagation. Mechanistically, the added cross‐gradient transport promotes convective detrainment and environmental mixing, which cools and moistens the mid‐troposphere, weakens downward momentum transport, alleviates excessive downwelling shortwave biases, and warms the surface temperature. The optimized rotation angle yields comparable improvements at 4‐km and 1‐km grid spacing, demonstrating resolution‐independent benefits across the CPM gray zone. By targeting the dynamical root of under‐mixed convective circulations, rather than tuning model microphysics or closures, OAS delivers robust, scale‐aware improvements in precipitation statistics, cloud vertical structure, and characteristics of MCS (MCSs), offering a practical pathway to more reliable CPM simulations for weather and climate applications.

CPM↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2020

Visualizing the enzyme mechanism of mevalonate diphosphate decarboxylase

Mevalonate diphosphate decarboxylases (MDDs) catalyze the ATP-dependent-Mg 2+ -decarboxylation of mevalonate-5-diphosphate (MVAPP) to produce isopentenyl diphosphate (IPP), which is essential in both eukaryotes and prokaryotes for polyisoprenoid synthesis. The substrates, MVAPP and ATP, have been shown to bind sequentially to MDD. Here we report crystals in which the enzyme remains active, allowing the visualization of conformational changes in Enterococcus faecalis MDD that describe sequential steps in an induced fit enzymatic reaction. Initial binding of MVAPP modulates the ATP binding pocket with a large loop movement. Upon ATP binding, a phosphate binding loop bends over the active site to recognize ATP and bring the molecules to their catalytically favored configuration. Positioned substrates then can chelate two Mg 2+ ions for the two steps of the reaction. Closure of the active site entrance brings a conserved lysine to trigger dissociative phosphoryl transfer of γ-phosphate from ATP to MVAPP, followed by the production of IPP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Vegetation forcing modulates global land monsoon and water resources in a CO 2 -enriched climate

The global monsoon is characterised by transitions between pronounced dry and wet seasons, affecting food security for two-thirds of the world’s population. Rising atmospheric CO 2 influences the terrestrial hydrological cycle through climate-radiative and vegetation-physiological forcings. How these two forcings affect the seasonal intensity and characteristics of monsoonal precipitation and runoff is poorly understood. Here we use four Earth System Models to show that in a CO 2 -enriched climate, radiative forcing changes drive annual precipitation increases for most monsoon regions. Further, vegetation feedbacks substantially affect annual precipitation in North and South America and Australia monsoon regions. In the dry season, runoff increases over most monsoon regions, due to stomatal closure-driven evapotranspiration reductions and associated atmospheric circulation change. Our results imply that flood risks may amplify in the wet season. However, the lengthening of the monsoon rainfall season and reduced evapotranspiration will shorten the water resources scarcity period for most monsoon regions.

58 GEOSCIENCES↗
DOE OSTI2020

Arabidopsis calcium-dependent protein kinase 3 regulates actin cytoskeleton organization and immunity

Pattern-triggered immunity and effector-triggered immunity are two primary forms of innate immunity in land plants. The molecular components and connecting nodes of pattern-triggered immunity and effector-triggered immunity are not fully understood. Here, we report that the Arabidopsis calcium-dependent protein kinase CPK3 is a key regulator of both pattern-triggered immunity and effector-triggered immunity. In vitro and in vivo phosphorylation assays, coupled with genetic and cell biology-based analyses, show that actin-depolymerization factor 4 (ADF4) is a physiological substrate of CPK3, and that phosphorylation of ADF4 by CPK3 governs actin cytoskeletal organization associated with pattern-triggered immunity. CPK3 regulates stomatal closure induced by flg22 and is required for resistance to Pst DC3000. Our data further demonstrates that CPK3 is required for resistance to Pst DC3000 carrying the effector AvrPphB. These results suggest that CPK3 is a missing link between cytoskeleton organization, pattern-triggered immunity and effector-triggered immunity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Conformational rearrangement of the NMDA receptor amino-terminal domain during activation and allosteric modulation

N-Methyl-D-aspartate receptors (NMDARs) are ionotropic glutamate receptors essential for synaptic plasticity and memory. Receptor activation involves glycine- and glutamate-stabilized closure of the GluN1 and GluN2 subunit ligand binding domains that is allosterically regulated by the amino-terminal domain (ATD). Using single molecule fluorescence resonance energy transfer (smFRET) to monitor subunit rearrangements in real-time, we observe a stable ATD inter-dimer distance in the Apo state and test the effects of agonists and antagonists. We find that GluN1 and GluN2 have distinct gating functions. Glutamate binding to GluN2 subunits elicits two identical, sequential steps of ATD dimer separation. Glycine binding to GluN1 has no detectable effect, but unlocks the receptor for activation so that glycine and glutamate together drive an altered activation trajectory that is consistent with ATD dimer separation and rotation. We find that protons exert allosteric inhibition by suppressing the glutamate-driven ATD separation steps, and that greater ATD separation translates into greater rotation and higher open probability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States

Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (R eff ) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with a median R eff below 1 when combined with either stay at home orders (median R eff 0.97, 95% confidence interval (CI) 0.58–1.39) or face masks (median R eff 0.97, 95% CI 0.58–1.39). While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Structural mechanisms of TRPV6 inhibition by ruthenium red and econazole

TRPV6 is a calcium-selective ion channel implicated in epithelial Ca 2+ uptake. TRPV6 inhibitors are needed for the treatment of a broad range of diseases associated with disturbed calcium homeostasis, including cancers. Here we combine cryo-EM, calcium imaging, and mutagenesis to explore molecular bases of human TRPV6 inhibition by the antifungal drug econazole and the universal ion channel blocker ruthenium red (RR). Econazole binds to an allosteric site at the channel’s periphery, where it replaces a lipid. In contrast, RR inhibits TRPV6 by binding in the middle of the ion channel’s selectivity filter and plugging its pore like a bottle cork. Despite different binding site locations, both inhibitors induce similar conformational changes in the channel resulting in closure of the gate formed by S6 helices bundle crossing. The uncovered molecular mechanisms of TRPV6 inhibition can guide the design of a new generation of clinically useful inhibitors.

59 BASIC BIOLOGICAL SCIENCES↗