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At least 163 records · Page 9

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NASA NTRS2023

The Statistical Emulators of GGCMI Phase 2: Responses of Year-to-Year Variation of Crop Yield to CO 2 , Temperature, Water and Nitrogen Perturbations

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO 2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO 2 –temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Climate change↗

DOE OSTI2021

Natural variation in the consequences of gene overexpression and its implications for evolutionary trajectories

Copy number variation through gene or chromosome amplification provides a route for rapid phenotypic variation and supports the long-term evolution of gene functions. Although the evolutionary importance of copy-number variation is known, little is understood about how genetic background influences its tolerance. Here, we measured fitness costs of over 4000 overexpressed genes in 15 Saccharomyces cerevisiae strains representing different lineages, to explore natural variation in tolerating gene overexpression (OE). Strain-specific effects dominated the fitness costs of gene OE. We report global differences in the consequences of gene OE, independent of the amplified gene, as well as gene-specific effects that were dependent on the genetic background. Natural variation in the response to gene OE could be explained by several models, including strain-specific physiological differences, resource limitations, and regulatory sensitivities. This work provides new insight on how genetic background influences tolerance to gene amplification and the evolutionary trajectories accessible to different backgrounds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Upwind Moisture Controls on Interannual Variations of Precipitation and Vegetation in China's Drylands

Dryland precipitation depends on upwind and local moisture sources via moisture recycling. How upwind moisture variations affect interannual variations of downwind precipitation and vegetation in China's drylands remains unclear. We used high-resolution moisture tracking data sets and found terrestrial moisture (93%) was the dominant moisture source for China's drylands, especially from drylands themselves (46%). In most dryland grids, we observed strong correlations between precipitation and upwind moisture sources from 2003 to 2022 (median r = 0.55), with a more significant effect in drier areas. These demonstrated the upwind moisture control on interannual variations of dryland precipitation, in which internal moisture from drylands exceeds the influence of external terrestrial sources. The upwind moisture variations, especially the recycled moisture of drylands, propagate to influence downwind vegetation greenness in precipitation-sensitive dryland areas. Our findings revealed that upwind moisture variations induced by climate or land-cover changes have important implications for water and food security in China's drylands.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Projective Integral Updates for High-Dimensional Variational Inference

Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over parameters to stand in for the full posterior. Capturing model variations that remain consistent with training data enables more robust predictions by reducing parameter sensitivity. This work introduces a fixed-point optimization for variational inference that is applicable when every feasible log density can be expressed as a linear combination of functions from a given basis. In such cases, the optimizer becomes a fixed-point of projective integral updates. When the basis spans univariate quadratics in each parameter, the feasible distributions are Gaussian mean-fields and the projective integral updates yield quasi-Newton variational Bayes (QNVB). Other bases and updates are also possible. Since these updates require high-dimensional integration, this work begins by proposing an efficient quasirandom sequence of quadratures for mean-field distributions. Each iterate of the sequence contains two evaluation points that combine to correctly integrate all univariate quadratic functions and, if the mean-field factors are symmetric, all univariate cubics. More importantly, averaging results over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. The corresponding variational updates require four loss evaluations with standard (not second-order) backpropagation to eliminate error terms from over half of all multivariate quadratic basis functions. Furthermore, this integration technique is motivated by first proposing stochastic blocked mean-field quadratures, which may be useful in other contexts. A PyTorch implementation of QNVB allows for better control over model uncertainty during training than competing methods. Experiments demonstrate superior generalizability for multiple learning problems and architectures.

Gaussian mean-field↗

DOE OSTI2021

The Diurnal Variation of the Aerosol Optical Depth at the ARM SGP Site

Abstract This study examines the diurnal variation of the aerosol optical depth (AOD) at 355 nm observed by Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site under both clear and cloudy‐sky conditions. Here only cloudy‐skies when the lidar signal is not fully attenuated are considered. The daytime AOD and its variation from the RL showed an excellent agreement with the Aerosol Robotic Network, demonstrating that the RL‐retrieved AOD is not affected by solar background contamination. The climatological annual‐mean daytime‐mean AOD is only slightly larger than the nighttime‐mean AOD (by 1%–3%). However, day‐to‐day variations are observed such that the daytime‐ and nighttime‐mean AOD difference for a given day can be large (about 95% of days have differences within 0.2). The seasonal AOD diurnal range (i.e., the difference between the maximum and minimum values) relative to the mean was 10%–15% except in the winter when it was 44%. The seasonal‐mean cloudy‐sky AOD diurnal variation is similar to that for clear‐sky, except that the AODs are larger (the annual‐mean cloudy‐sky AOD is larger than the clear‐sky by 24%). The aerosol lidar ratio diurnal variations are also examined, which are 10%–20% for all seasons with a minimum near 9 a.m. to 15 p.m. for all seasons except winter. Also presented is the annual‐mean AOD from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite at SGP site: its daytime AOD is about 0.1 smaller than nighttime AOD because of daytime solar background contamination.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology

Soil microbiomes are rapidly becoming known as an important driver of plant phenotypic variation and may mediate plant responses to environmental factors. However, integrating spatial scales relevant to climate change with plant intraspecific genetic variation and soil microbial ecology is difficult, making studies of broad inference rare. Here we hypothesize and show: 1) the degree to which tree genotypes condition their soil microbiomes varies by population across the geographic distribution of a widespread riparian tree, Populus angustifolia; 2) geographic dissimilarity in soil microbiomes among populations is influenced by both abiotic and biotic environmental variation; and 3) soil microbiomes that vary in response to abiotic and biotic factors can change plant foliar phenology. We show soil microbiomes respond to intraspecific variation at the tree genotype and population level, and geographic variation in soil characteristics and climate. Using a fully reciprocal plant population by soil location feedback experiment, we identified a climate-based soil microbiome effect that advanced and delayed bud break phenology by approximately 10 days. These results demonstrate a landscape-level feedback between tree populations and associated soil microbial communities and suggest soil microbes may play important roles in mediating and buffering bud break phenology with climate warming, with whole ecosystem implications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

On the statistical theory of self-gravitating collisionless dark matter flow: Scale and redshift variation of velocity and density distributions

The statistics of velocity and density fields are crucial for cosmic structure formation and evolution. Here, this paper extends our previous work on the two-point second-order statistics for the velocity field [Phys. Fluids 35, 077105 (2023)] to one-point probability distributions for both density and velocity fields. The scale and redshift variation of density and velocity distributions are studied by a halo-based non-projection approach. First, all particles are divided into halo and out-of-halo particles so that the redshift variation can be studied via generalized kurtosis of distributions for halo and out-of-halo particles, respectively. Second, without projecting particle fields onto a structured grid, the scale variation is analyzed by identifying all particle pairs on different scales $r$. We demonstrate that: (i) Delaunay tessellation can be used to reconstruct the density field. The density correlation, spectrum, and dispersion functions were obtained, modeled, and compared with the N-body simulation; (ii) the velocity distributions are symmetric on both small and large scales and are non-symmetric with a negative skewness on intermediate scales due to the inverse energy cascade on small scales with a constant rate $\varepsilon_u$; (iii) On small scales, the even order moments of pairwise velocity $\Delta u_L$ follow a two-thirds law $\propto{(-\varepsilon_ur)}^{2/3}$, while the odd order moments follow a linear scaling $\langle(\Delta u_L)^{2n+1}\rangle=(2n+1)\langle(\Delta u_L)^{2n}\rangle\langle\Delta u_L\rangle\propto{r}$; (iv) The scale variation of the velocity distributions was studied for longitudinal velocities $u_L$ or $u_L^{'}$, pairwise velocity (velocity difference) $\Delta u_L$=$u_L^{'}$-$u_L$ and velocity sum $\Sigma u_L$=$u^{'}_L$+$u_L$. Fully developed velocity fields are never Gaussian on any scale, despite that they can initially be Gaussian; (v) On small scales, $u_L$ and $\Sigma u_L$ can be modeled by a $X$ distribution to maximize the entropy of the system. The distribution of $\Delta u_L$ can be different; (vi) On large scales, $\Delta u_L$ and $\Sigma u_L$ can be modeled by a logistic or a $X$ distribution, while $u_L$ has a different distribution; (vii) the redshift variation of the velocity distributions follows the evolution of the $X$ distribution involving a shape parameter $\alpha(z)$ decreasing with time.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters

In scientific applications, predictive modeling is often of limited use without accurate uncertainty quantification (UQ) to indicate when a model may be extrapolating or when more data needs to be collected. Bayesian Neural Networks (BNNs) produce predictive uncertainty by propagating uncertainty in neural network (NN) weights and offer the promise of obtaining not only an accurate predictive model but also accurate UQ. However, in practice, obtaining accurate UQ with BNNs is difficult due in part to the approximations used for model training (such as those made in variational inference) and in part to the need to choose a suitable set of hyperparameters; these hyperparameters outnumber those needed for traditional NNs and often have opaque effects on the results. We aim to shed light on the effects of hyperparameter choices for variational BNNs by performing a global sensitivity analysis of variational BNN performance under varying hyperparameter settings. Our results indicate that many of the hyperparameters interact with each other to affect both predictive accuracy and UQ. For improved usage of variational BNNs in real-world applications, we suggest that thorough hyperparameter tuning, including tuning of prior hyperparameters and loss function parameters, is essential for accurate UQ in variational BNNs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Shallow-circuit variational quantum eigensolver based on symmetry-inspired Hilbert space partitioning for quantum chemical calculations

Development of resource-friendly quantum algorithms remains highly desirable for noisy intermediate-scale quantum computing. Based on the variational quantum eigensolver (VQE) with unitary coupled-cluster Ansatz, we demonstrate that partitioning of the Hilbert space made possible by the point-group symmetry of the molecular systems greatly reduces the number of variational operators by confining the variational search within a subspace. In addition, we found that instead of including all subterms for each excitation operator, a single-term representation suffices to reach required accuracy for various molecules tested, resulting in an additional shortening of the quantum circuit by a factor of 4–8. With these strategies, VQE calculations on a noise-free quantum simulator achieve energies within a few meVs of those obtained with the full unitary coupled-cluster Ansatz with single and double excitations for the H 4 -square, H 4 -chain, and H 6 -hexagon molecules, while the number of cnot gates, a measure of the quantum-circuit depth, is reduced by a factor of as large as 35. Furthermore, we introduced an efficient “score” parameter to rank the excitation operators, so that the operators causing larger energy reduction can be applied first. Using the H 4 square and H 4 chain as examples, We demonstrated on noisy quantum simulators that the first few variational operators can bring the energy within the chemical accuracy, while additional operators do not improve the energy since the accumulative noise outweighs the gain from the expansion of the variational Ansatz.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

A Comparative Study of the Perceptual Sensitivity of Topological Visualizations to Feature Variations

Color maps are a commonly used visualization technique in which data are mapped to optical properties, e.g., color or opacity. Color maps, however, do not explicitly convey structures (e.g., positions and scale of features) within data. Topology-based visualizations reveal and explicitly communicate structures underlying data. Although our understanding of what types of features are captured by topological visualizations is good, our understanding of people's perception of those features is not. Further, this paper evaluates the sensitivity of topology-based isocontour, Reeb graph, and persistence diagram visualizations compared to a reference color map visualization for synthetically generated scalar fields on 2-manifold triangular meshes embedded in 3D. In particular, we built and ran a human-subject study that evaluated the perception of data features characterized by Gaussian signals and measured how effectively each visualization technique portrays variations of data features arising from the position and amplitude variation of a mixture of Gaussians. For positional feature variations, the results showed that only the Reeb graph visualization had high sensitivity. For amplitude feature variations, persistence diagrams and color maps demonstrated the highest sensitivity, whereas isocontours showed only weak sensitivity. These results take an important step toward understanding which topology-based tools are best for various data and task scenarios and their effectiveness in conveying topological variations as compared to conventional color mapping.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Genotype–environment associations across spatial scales reveal the importance of putative adaptive genetic variation in divergence

Abstract Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide‐ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype–environment associations in the hermit thrush ( Catharus guttatus ), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype–environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature‐associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature‐related barriers to gene flow and/or temperature‐driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype–environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population‐level sensitivity to climate change and other stressors.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Adaptive variation and plasticity in non‐structural carbohydrate storage in a temperate tree species

Abstract Trees' total amount of non‐structural carbohydrate (NSC) stores and the proportion of these stores residing as insoluble starch are vital traits for individuals living in variable environments. However, our understanding of how stores vary in response to environmental stress is poorly understood as the genetic component of storage is rarely accounted for in studies. Here, we quantified variation in NSC traits in branch samples taken from over 600 clonally transplanted black cottonwood ( Populus trichocarpa) trees grown in two common gardens. We found heritable variation in both total NSC stores and the proportion of stores in starch (H 2 TNC = 0.19, H 2 PropStarch = 0.31), indicating a substantial genetic component of variation. In addition, we found high amounts of plasticity in both traits in response to cold temperatures and significant genotype‐by‐environment (GxE) interactions in the total amount of NSC stored (54% of P is GxE). This finding of high GxE indicates extensive variation across trees in their response to environment, which may explain why previous studies of carbohydrate stores' responses to stress have failed to converge on a consistent pattern. Overall, we found high amounts of environmental and genetic variation in NSC storage concentrations, which may bolster species against future climate change.

Blumstein, Meghan↗

DOE OSTI2023

Effects of Part-to-Part Flow Variations on Overall Effectiveness and Life of Rotating Turbine Blades

Abstract As firing temperatures in gas turbine engines continue to increase to achieve high efficiencies, components in the main gas path must be protected with cooling flows to ensure lifing targets are met. Manufacturing variations, however, influence the performance and life characteristics of components with the same nominal design. This study presents blade flow and overall cooling effectiveness measurements for nine true-scale, aero engine turbine blades with realistic manufacturing variations. Flow measurements were made through each blade at a fixed pressure ratio to determine flow variability between holes and between blades. Infrared thermography was used to capture spatially-resolved temperature measurements reported as overall effectiveness on the same nine blades under high-speed rotating conditions at the Steady Thermal Aero Research Turbine Laboratory. Thermal performance was correlated with blade flow performance indicating substantial blade-to-blade variations resulting from manufacturing differences. Measurements also indicated wide variations in cooling jet trajectories as well as overall cooling effectiveness. Finally, the observed blade-to-blade variations in effectiveness were scaled to engine conditions with lifing estimates showing some blades would be expected to last only half as long as others due to manufacturing variability.

Engineering↗

DOE OSTI2023

Acute stress reduces population-level metabolic and proteomic variation

Abstract Background Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. Results We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. Conclusions RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

An Agnostic Approach to Building Empirical Type Ia Supernova Light Curves: Evidence for Intrinsic Chromatic Flux Variation Using Nearby Supernova Factory Data

We present a new empirical Type Ia supernova (SN Ia) model with three chromatic flux variation templates: one phase dependent and two phase independent. No underlying dust extinction model or patterns of intrinsic variability are assumed. Implemented with Stan and trained using spectrally binned Nearby Supernova Factory spectrophotometry, we examine this model's 2D, phase-independent flux variation space using two motivated basis representations. In both, the first phase-independent template captures variation that appears dust-like, while the second captures a combination of effectively intrinsic variability and second-order dust-like effects. We find that ≈13% of the modeled phase-independent flux variance is not dust-like. Previous empirical SN Ia models either assume an effective dust extinction recipe in their architecture, or only allow for a single mode of phase-independent variation. The presented results demonstrate such an approach may be insufficient, because it could "leak" noticeable intrinsic variation into phase-independent templates.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models

Abstract. In the current global climate models (GCMs), the nonlinearity effect of subgrid cloud variations on the parameterization of warm-rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm-rain process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in situ measurements from a recent field campaign and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that tends to make EF < 1. This effect is especially strong at cloud top, where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc is significantly smaller than its counterpart that accounts only for the subgrid variation of qc, especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1972

Structure and variations of the heterosphere

Atmospheric composition is kept almost homogeneous to a height of approximately 90 km (the homosphere) by mixing; above 100 km (the heterosphere), diffusive separation of the atmospheric constituents occurs. The heterosphere is dominated by two energy sources: solar extreme ultraviolet radiation and the solar wind. Many large variations are observed in this region and they can all be related, directly or indirectly, to variations in the intrinsic intensity of these two sources and to variations in the location of the sources relative to the observer. Atmospheric models can be successfully constructed in which the temperature and density variations are empirically related to solar and geomagnetic parameters. Some of the more recent findings concerning the structure and variations in the heterosphere are presented.

Jacchia, L. G.↗

NASA NTRS1973

Short and long periodic atmospheric variations between 25 and 200 km

Previously collected data on atmospheric pressure, density, temperature and winds between 25 and 200 km from sources including Meteorological Rocket Network data, ROBIN falling sphere data, grenade release and pitot tube data, meteor winds, chemical release winds, satellite data, and others were analyzed by a daily difference method and results on the distribution statistics, magnitude, and spatial structure of gravity wave and planetary wave atmospheric variations are presented. Time structure of the gravity wave variations were determined by the analysis of residuals from harmonic analysis of time series data. Planetary wave contributions in the 25-85 km range were discovered and found to have significant height and latitudinal variation. Long period planetary waves, and seasonal variations were also computed by harmonic analysis. Revised height variations of the gravity wave contributions in the 25 to 85 km height range were computed. An engineering method and design values for gravity wave magnitudes and wave lengths are given to be used for such tasks as evaluating the effects on the dynamical heating, stability and control of spacecraft such as the space shuttle vehicle in launch or reentry trajectories.

Justus, C. G.↗