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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SST-TG-P7F4R3200: Decaying Stably-Stratified Turbulence (SST), Initialized Using Taylor-Green Vortices (TG) at Prandtl Number Pr=7, Froude Number Fr=4, Reynolds Number Re=3200

This dataset comprises direct numerical simulations (DNS) of decaying stably-stratified turbulence influenced by a linear background density gradient, initialized using an array of Taylor-Green vortices, extending the Pr=1 simulations performed in [Riley and de Bruyn Kops (2003)](https://doi.org/10.1063/1.1578077). The initial Prandtl, Froude, and Reynolds numbers are (Pr, Fr, Re) = (7, 4, 3200). A total of 15,250 snapshots are recorded at uniform time intervals, each with a spatial resolution of 1280x1280x640 grid points. Four flow variables are associated with each snapshot: the three velocity components (u,v,w) and the perturbed density field (rho) away from the background gradient. All fields are stored in binary format (32-bit little-endian), each with a size of 4 GB, yielding a total dataset size of 244 TB. Further details are referenced in the attached README file, and a current list of publications and associated analysis tools are provided at https://stratified-turbulence.github.io/web/.

42 ENGINEERING↗
DOE OSTI2026

Lessons Learned from Ecosystem-Scale Experimental Field Studies (Workshop Report)

Efforts to understand and predict ecosystem responses to environmental change require long-term, large-scale, spatially representative experiments and observations that capture natural variability, test predictive models, and generate transferable knowledge. Such studies are indispensable for unraveling the complexities of terrestrial ecosystems and their responses to disturbances and evolving environmental conditions, while generating the data necessary for developing mechanistic models and predictive tools that inform decision-making processes. Having a rich history of designing and executing large-scale ecosystem experiments, the U.S. Department of Energy’s Environmental System Science program convened a workshop in January 2025 that brought together leaders in the field to distill critical lessons from decades of experience in large-scale experiments. The workshop aimed to (1) provide an ecosystem experiment primer for best practices, thus ensuring a high scientific return on investment for funding agencies, and (2) offer a robust framework for the design and management of future research initiatives. This report synthesizes insights and experiences from workshop participants and is structured to capture the entire research life cycle, from goal setting and design to operations, adaptive management, team dynamics, collaborations, and the often overlooked aspect of decommissioning. By synthesizing decision-making and lessons learned across diverse research approaches, the report aims to provide a template of essential factors to consider when designing successful long-term, large-scale ecosystem experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

High-Magnification Faraday Rotation Imaging and Analysis of X-pinch Implosion Dynamics

An X-pinch load driven by an intense current pulse (>100 kA in ~100 ns) can result in the formation of a small radius, runaway compressional micro-pinch. A micro-pinch is characterized by a hot (>1 keV), current-driven (>100 kA), high-density plasma column (near solid density) with a small neck diameter (1–10 μm), a short axial extent (<1 mm), and a short duration (.1 ns). With material pressures often well into the multi-Mbar regime, a micro-pinch plasma often radiates an intense, sub-ns burst of sub-keV to multi-keV x-rays. A low-density coronal plasma immediately surrounding the dense plasma neck could potentially shunt current away from the neck and thus reduce the magnetic drive pressure applied to the neck. To study the current distribution in the coronal plasma throughout the implosion process, a Faraday rotation imaging diagnostic (1064 nm) capable of producing simultaneous high-magnification polarimetric and interferometric images has been developed for the 1-MA, 100-ns MAIZE facility at the University of Michigan. Designed with a variable magnification (1–10&times;), this diagnostic achieves a spatial resolution of approximately 35 μm, which is useful for resolving the ~100-μm-scale coronal plasma region immediately surrounding the dense core. Combined with the corresponding interferogram, which provides the radial density distribution in the low-density coronal plasma, the measured rotation can be used to diagnose the delivery of the driver current, and thus the magnetic drive pressure, to the micro-pinch plasma as it approaches minimal radius. Using this technique on MAIZE, we have measured

Dowhan, G. V.↗
DOE OSTI2024

Condition-Based Maintenance of a Circulating Water System of a Canadian Nuclear Power Plant using Machine Learning and Statistical Tools

Canada Deuterium Uranium pressurized-heavy-water reactors (PHWR) are a type of nuclear power plant that generate clean and reliable energy. The scope of this work is to automate data analysis methodologies to inform a condition-based maintenance strategy of a circulating water system (CWS) of a PHWR. The multiunit CWS provides a continuous supply of water to cool steam condensers, even during transient scenarios, thereby improving the thermal efficiency. This work aims to develop a machine learning (ML) based approach to detect anomalies in heterogeneous data of a CWS in a PHWR to help inform a predictive maintenance strategy. The heterogeneous data include textual and numeric time series data for a PHWR. Natural-language-processing (NLP)-based models are used to analyze textual data contained in work orders and operator logs and an event-timeseries correlation detection method is applied to assist anomalies diagnoses for CWS. An ML model Robust Linear Model (RLM) is also used to remove the seasonal variations in the system variable distributions based on distributions of environmental variables. A machine learning model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), trained on both original data and data without any seasonal variations will then be used to detect if an anomaly exists. Thus, by moving to an automated methodology to detect, classify, and forecast anomalies, the maintenance strategy would be based on component condition instead of a time-based schedule.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

GPS-Based Gamma Survey for Characterizing and Decommissioning NORM Sites - 20389

Gamma survey techniques are an especially powerful decommissioning tool at naturally occurring radioactive material (NORM) sites due to both the low cost to obtain data over a large spatial scale and the abundance of gamma emitters in the uranium and thorium decay series. Gamma surveys are executed by coupling a detector - most often a sodium iodide crystal - to a global positioning system (GPS), then reporting a location and gross gamma reading coincidentally to a data logger. Systems may be carried by workers or mounted to a car, all-terrain vehicle, or unmanned aerial system (UAS). The resulting data set provides a high-resolution but low precision map of the gamma radiation field over the area surveyed. Frequently this map is also correlated to soil concentrations of NORM radionuclides (most often, Ra-226) and/or exposure rate. Gamma survey parameters such as movement speed, transect spacing, and data logging frequency define the spatial resolution of the resulting surface, and can be optimized depending on the desired survey sensitivity. This paper examines gamma survey as a tool for decommissioning NORM sites and provides an overview of current gamma survey technology designed to improve the efficiency and effectiveness of the decommissioning process. Topics to be discussed in the paper include: - An overview of gamma survey systems, and the utility of different delivery vehicles depending on desired cost, desired spatial resolution, and site topography. - The influence of physical detector characteristics on detection sensitivity and survey planning. - The tradeoff between high-resolution and large spatial extent, but inherently uncertain data, and low-resolution, low spatial extent, but highly certain data, as well as the specific utility of each of these types of data during NORM facility decommissioning. - Confounding variables that may limit the utility of gamma survey at some sites (e.g., radon gas and spatial heterogeneity / hot spots), and methods to plan for and control these conditions. Results show that the confounding variables, such as radon and data output can greatly influence the overall data quality associated with the decommissioning process. In addition, the use of real-time and aerial survey platforms provides a method for ensuring proper spatial extent of the data. When applied thoughtfully, gamma survey is a powerful tool for detecting NORM radionuclides in the environment and a cost-effective technique for identifying areas requiring remediation. However, entities performing or using gamma survey as a decommissioning tool must be aware of both its advantages and its limitations before basing remediation or regulatory action on gamma survey results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Quantification of spatial and seasonal trends in the atmosphere and construction of statistical models for infrasonic propagation

SUMMARY Infrasonic waves are influenced by variations in the density, pressure and temperature as well as the ambient winds. Modelling infrasonic propagation can be challenging due to the dynamic nature of the atmosphere as well as the sparseness of measurements which result in variability and notable uncertainty. A framework is presented to quantify spatial and seasonal trends in atmospheric structure via analysis of the effective sound speed profile and identification of temporal trends in the middle atmospheric waveguide produced by the circumpolar vortex winds. Seasonal definitions identifying typical atmospheric structures during the summer, winter and spring/fall transition periods are identified using atmospheric data from 2010 through 2020. Seasonal trend analysis is conducted for a number of locations across the contiguous United States to quantify spatial variations in atmospheric structure that impact infrasonic propagation. For each season and location, empirical orthogonal function analysis is used to reduce the historical archive of atmospheric data into a smaller representative set that can be analysed using numerical tools more efficiently. Infrasonic ray tracing and finite-frequency modal propagation analyses are applied to construct propagation path geometry and transmission loss statistics which are useful in localization and yield estimation for infrasonic sources, respectively. An example application is detailed in which transmission loss statistics are combined with an explosive source model and noise statistics to quantify the capability of a network to detect nearby sources.

58 GEOSCIENCES↗
DOE OSTI2024

Characterizing the multisectoral impacts of future global hydrologic variability

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Soil Moisture Buffers the Impact of Precipitation Variability on Ecosystem Productivity

Water availability governs ecosystem productivity, yet estimates of vegetation sensitivity to water can differ greatly depending on whether the sensitivity is examined spatially or temporally. In particular, the spatial sensitivity is often reported to be much stronger than temporal sensitivities, leading to highly uncertain projections of ecosystem responses to future climate change when using space-for-time substitution. The large difference between spatial and temporal sensitivities remains unexplained. Prior research, however, primarily relied on precipitation as the water availability proxy, whereas vegetation responds to soil moisture. Here, we combined satellite estimates of vegetation productivity with soil moisture data across water-limited ecosystems of the continental United States (CONUS) to identify a convergent sensitivity of productivity to water availability. Using precipitation, we show that temporal sensitivity is 66% lower than spatial sensitivity overall. Our analysis identified the cause of the difference to be primarily driven by the seasonal variability of water availability, rooting depth, and soil properties. When using soil moisture instead of precipitation, we observed widespread convergence in the spatial and temporal sensitivities—that is, the two sensitivities became much more similar in magnitude across all water-limited ecosystems within CONUS. These results show that overlooking soil hydrology can inflate perceived discrepancies between spatial and temporal vegetation sensitivities, leading to biased projections of ecosystem dynamics under future hydro-climatic change.

Wang, Huiqi [University of California, Berkeley, C↗
DOE OSTI2020

A Satellite-Derived Upper-Ocean Stratification Data Set for the Tropical North Atlantic With Potential Applications for Hurricane Intensity Prediction

Upper-ocean stratification strongly impacts vertical mixing and the heat flux between the ocean and atmosphere, especially under extreme conditions of tropical cyclones (TCs). Knowledge of prestorm stratification is important for accurate TC intensity prediction. In situ observations of the tropical ocean have significantly increased in the past decade. However, they are still too sparse to resolve ocean stratification variability in near-real time and on small spatial scales. Here, in this study, based on long-term observations and an ocean reanalysis data set from 2004–2017, we investigate the possibility of retrieving upper-ocean stratification from sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) using a simple regression method. It is found that more than 90% of the mean seasonal cycle and about 30% to 80% of temperature and salinity stratification anomalies can be reconstructed using surface data from either observations or an ocean reanalysis. Simple regression can be used with satellite observations to create a high-resolution, near-real-time-gridded ocean stratification data set that successfully reproduces both the large and mesoscale variability of ocean stratification. When used in a simple expression for TC-induced SST cooling, the satellite-derived stratification shows improvements over an ocean analysis in terms of variance explained of SST cooling, offering promise as a near-real-time indicator of the ocean's impact on TC intensification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Genetic Identification of Lamprey Genera and Anadromous Ecotypes in Watersheds of the Northeastern Pacific Ocean

ABSTRACT Nonparasitic, nonmigratory Western Brook Lamprey (WBL; Lampetra ayresii ), and parasitic, anadromous Western River Lamprey (WRL; L. ayresii ) are sympatric lampreys that likely represent different life history variations of a single species. Novel genetic tools are critical for differentiating WBL and WRL, whose larvae preclude morphological identification (ID) and will enable comprehensive assessment of imperiled native lampreys of the Northeastern Pacific (including WBL, WRL, and Pacific Lamprey, Entosphenus tridentatus ). We developed 47 candidate single nucleotide polymorphism (SNP) markers using whole genome resequencing of WBL ( N = 24) and WRL ( N = 15) from Ksi Ts'oohl Ts'ap Creek (Nass River, British Columbia, Canada) which are likely ecotypes distinguished by few divergent SNPs across multiple chromosomes. We used five novel candidate SNPs to perform genetic ID of WBL and WRL ecotypes in collections of mixed native lampreys from lower Columbia River tributaries ( N = 1474), Ksi Ts'oohl Ts'ap Creek ( N = 352), and ocean phase WRL from the Georgia Basin (Salish Sea, British Columbia, Canada; N = 91). Two previously published SNPs were used to ID genera, Entosphenus versus Lampetra . Morphological ID utilized photographs collected from a subset of genotyped lampreys, and high concordance was demonstrated between ID methods for genera (99%) and Lampetra ecotypes (> 98%). We characterized spatial and temporal composition of lamprey genera and ecotypes surveyed across NE Pacific tributaries under the expectation these compositions would be similar across nearby sites and across years at the same site. Proportions of lamprey genera were highly variable within regions and across years; however, Lampetra ecotypic proportions were spatially and temporally stable. WRL were rare in lower Columbia tributaries (~1% average rate among Lampetra ) and common further north (> 40% of Lampetra ). Genetic ID methods are powerful monitoring tools that create the novel ability to ascertain genera and ecotypes regardless of life stage, while increasing the efficiency of surveys by eliminating time‐intensive morphological data collection.

Silver, G. S. [Columbia River Inter‐Tribal Fish Co↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2022

Understanding the Drivers of Atlantic Multidecadal Variability using a Stochastic Model Hierarchy

The relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. Comparisons between climate models with SLAB and fully-dynamic (FULL) ocean components are often used to explore this question, but cannot reveal how individual ocean processes generate these differences. We build a hierarchy of physically interpretable stochastic models to investigate the contribution of two upper-ocean processes to AMV: the role of seasonal variation and mixed-layer entrainment. This interpretability arises from the stochastic model’s simplified representation of sea surface temperature (SST), considering only the local upper ocean response to white-noise atmospheric forcing and its impact on surface heat exchange. We focus on understanding differences between SLAB and FULL non-eddy resolving pre-industrial control simulations of the Community Earth System Model 1 (CESM), and estimate the stochastic model parameters from each respective simulation. Despite its simplicity, the stochastic model reproduces temporal characteristics of SST variability in the SPG, including reemergence, seasonal-to-interannual persistence and power spectra. Furthermore, unrealistically persistent SST of the CESM-SLAB ocean simulation is reproduced in the equivalent stochastic model configuration where the mixed-layer depth (MLD) is constant. The stochastic model also reveals that vertical entrainment primarily damps SST variability, thus explaining why SLAB exhibits larger SST variance than FULL. Here, the stochastic model driven by temporally stochastic, spatially coherent forcing patterns reproduces the canonical AMV pattern. However, the amplitude of low-frequency variability remains underestimated, suggesting a role for ocean dynamics beyond entrainment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Dynamic Amplification of Subtropical Extreme Precipitation in a Warming Climate

Projected precipitation changes in a warming climate vary considerably, spatially, and between intensities. The changes can be greater or less than the ~7% K -1 Clausius-Clapeyron (CC) prediction, owing to dynamic effects. Using two global-climate-model large ensembles, we quantify the dynamically induced changes to precipitation extremes from the present (1996–2005) to late-21st-century (2071–2080) climates, as a function of recurrence interval, focusing on the subtropics. We separate non-CC changes into a term proportional to the present-day vertical-velocity spatial pattern (i.e., an amplification or damping thereof by a constant factor) and a residual. The amplitude term varies with recurrence interval, approximately canceling (doubling) CC for moderate (large) extremes, increasing precipitation variability. Contrastingly, the residual is quasi-uniform across recurrence intervals but spatially heterogeneous, weakening extremes over dry zones. This residual may be related to Hadley cell expansion, although this explanation is insufficient to explain many features, and other possible mechanisms are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Detecting multi‐scale riverine topographic variability and its influence on Chinook salmon habitat selection

Abstract Quantifying geomorphic conditions that impact riverine ecosystems is critical in river management due to degraded riverine habitat, changing flow and thermal conditions, and increasing anthropogenic pressure. Geomorphic complexity at different scales directly impacts habitat heterogeneity and affects aquatic biodiversity resilience. Here we showed that the combination of continuous spatial survey at high resolution, topobathymetric light detection and ranging (LiDAR), and continuous wavelet analysis can help identify and characterize that complexity. We used a continuous wavelet analysis on 1‐m resolution topobathymetry in three rivers in the Salmon River Basin, Idaho (USA), to identify different scales of topographic variability and the potential effects of this variability on salmonid redd site selection. On each river, wavelet scales characterized the topographic variability by portraying repeating patterns in the longitudinal profile. We found three major representative spatial wavelet scales of topographic variability in each river: a small wavelet scale associated with local morphology such as pools and riffles, a mid‐wavelet scale that identified larger channel unit features, and a large wavelet scale related to valley‐scale controls. The small wavelet scale was used to identify pools and riffles along the entire lengths of each river as well as areas with differing riffle‐pool development. Areas along the rivers with high local topographic variability (high wavelet power) at all wavelet scales contained the largest features (i.e., deepest or longest pools) in the systems. By comparing the wavelet power for each wavelet scale to Chinook salmon redd locations, we found that higher small‐scale wavelet power, which is related to pool‐riffle topography, is important for redd site selection. The continuous wavelet methodology objectively identified scales of topographic variability present in these rivers, performed efficient channel‐unit identification, and provided geomorphic assessment without laborious field surveys.

Duffin, Jenna↗
DOE OSTI2020

Evaluating proxy influence in assimilated paleoclimate reconstructions – Testing the exchangeability of two ensembles of spatial processes

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. In this study, we apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Entanglement structures in quantum field theories: Negativity cores and bound entanglement in the vacuum

Here, the many-body entanglement between two finite (size-d) disjoint vacuum regions of noninteracting lattice scalar field theory in one spatial dimension, i.e., a (d A × d B ) mixed Gaussian continuous variable system, is locally transformed into a tensor-product core of (1 A × 1 B ) mixed entangled pairs. Accessible entanglement within these core pairs exhibits an exponential hierarchy and as such identifies the structure of dominant region modes from which vacuum entanglement could be extracted into a spatially separated pair of quantum detectors. Beyond the core, the remaining modes of the halo are determined to be AB separable in isolation, as well as separable from the core. However, state preparation protocols that distribute entanglement in the form of (1 A × 1 B ) mixed core pairs are found to require additional entanglement in the halo that is obscured by classical correlations. This inaccessible (bound) halo entanglement is found to mirror the accessible entanglement, but with a step behavior as the continuum is approached. It remains possible that alternate initialization protocols that do not utilize the exponential hierarchy of core-pair entanglement may require less inaccessible entanglement. Entanglement consolidation is expected to persist in higher dimensions and may aid classical and quantum simulations of asymptotically free gauge field theories, such as quantum chromodynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗