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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Feedstock variability impacts the bioconversion of sugar and lignin streams derived from corn stover by Clostridium tyrobutyricum and engineered Pseudomonas putida

Abstract Feedstock variability represents a challenge in lignocellulosic biorefineries, as it can influence both lignocellulose deconstruction and microbial conversion processes for biofuels and biochemicals production. The impact of feedstock variability on microbial performance remains underexplored, and predictive tools for microbial behaviour are needed to mitigate risks in biorefinery scale‐up. Here, twelve batches of corn stover were deconstructed via deacetylation, mechanical refining, and enzymatic hydrolysis to generate lignin‐rich and sugar streams. These batches and their derived streams were characterised to identify their chemical components, and the streams were used as substrates for producing muconate and butyrate by engineered Pseudomonas putida and wildtype Clostridium tyrobutyricum , respectively. Bacterial performance (growth, product titers, yields, and productivities) differed among the batches, but no strong correlations were identified between feedstock composition and performance. To provide metabolic insights into the origin of these differences, we evaluated the effect of twenty‐three isolated chemical components on these microbes, including three components in relevant bioprocess settings in bioreactors, and we found that growth‐inhibitory concentrations were outside the ranges observed in the streams. Overall, this study generates a foundational dataset on P. putida and C. tyrobutyricum performance to enable future predictive models and underscores their resilience in effectively converting fluctuating lignocellulose‐derived streams into bioproducts.

09 BIOMASS FUELS↗
DOE OSTI2021

Soil respiration spatial and temporal variability in China between 1961 and 2014

Soil respiration (Rs) plays an important role in terrestrial-atmospheric carbon exchange but remains one of the least studied components of the carbon cycle. How environmental changes influence Rs, and in turn, how Rs influences terrestrial carbon storage in China is unclear. Here, we estimated spatial patterns and temporal trends in Rs from 1961 to 2014 to determine the influence of the recent warming hiatus on the Rs temporal variability in China. We evaluated the relationship between Rs and a set of environmental factors and developed biome-specific Rs models that were parameterized based on 2,862 Rs records and 18 continuous variables (e.g., air temperature, precipitation and leaf area index). Based on the biome-specific Rs models and environmental information, we estimated the annual Rs as well as the change trend of Rs in China during the study period. The mean annual Rs from 1961 to 2014 was 3.50 (confidence interval +/- 0.87) Pg center dot C center dot year(-1). Rs in China increased at a rate of 0.008 Pg center dot C center dot year(-2) between 1982 and 1998 (warming period), but was static between 1999 and 2014 (warming hiatus). This reduction in Rs increase rate may have an important role in enhancing terrestrial carbon sequestration. These findings confirm that temporal heterogeneity in Rs could be influenced by regional environmental changes, and further help in improving the understanding of soil carbon dynamics in China.

Jian, Jinshi↗
DOE OSTI2022

Quantifying the effects of varietal types × management on the spatial variability of sorghum biomass across US environments

Abstract Regional‐scale estimations of sorghum biomass production allow identification of optimum genotype×environment×management (G×E×M) combinations for bioenergy generation. The objective of this study was to determine the degree of contributions of G, E, and M toward variability in sorghum biomass in the United States. Using the Agricultural Production Systems sIMulator in a grid computing platform, biomass was simulated for irrigated and rainfed conditions for 30 years across the United States for four sorghum varietal types (grain—GS, sudangrass—SS, photosensitive—PS, and photo‐insensitive—PI). Simulated biomass was assessed by environments clustered using the sum of intercepted solar radiation ( ir ), mean of temperature stress factor ( tp ) and water stress factor ( sw ). Simulated biomass ranged from 5.8 t ha −1 (GS‐rainfed) to 27.5 t ha −1 (PI‐irrigated). Under high‐temperature environments (mean annual temperature = 25°C), rainfed biomass between 40 and 80 days after planting (DAP) was strongly correlated with sw ( r = 0.64–0.86) and irrigated biomass with ir ( r = 0.68–0.81). Under low‐temperature environments (mean annual temperature = 18°C) after 40 DAP, tp and ir had greater effects than sw ( r = 0.55–0.82). Biomass variance was mainly explained by varietal type (50%–76%) in all environments×irrigation combinations, except in the high‐ and mid‐temperature environments under rainfed conditions where rainfall had the major effect (25%–45%). However, when mean temperature during the growing season decreased from 25°C (high environments) to 18°C (low environments), the contribution of mean temperature to biomass variance increased from 7% to 34% (rainfed) and from 4% to 36% (irrigated). Varietal type had the larger interactions with other factors independently of the environment and irrigation. We demonstrated a need to quantify (i) the main G×E×M drivers of biomass variability based on environmental stress factors and (ii) the variance contribution of these drivers on sorghum biomass. Our regional‐scale estimations are key inputs for future robust biomass projections of energy sorghum genotypes integrating G×E×M under climate change scenarios.

Ojeda, Jonathan J.↗
DOE OSTI2020

Seasonal to Decadal Variability in Focused Groundwater and Contaminant Discharge along a Channelized Stream

Abstract Focused groundwater discharge to streams is problematic at contaminated sites because high fluxes can limit natural attenuation in the hyporheic zone. However, information on location, spatial evolution, and temporal persistence of springs in unlithified sediments over multiyear time scales is limited. We examine discharge at point (~1‐m) to reach (~300‐m) scales along a stream that intercepts trichloroethene and technetium‐99 plumes from a Superfund site. During 2011 to 2012, we seasonally monitored stream and spring flow and contaminant concentrations, along with probing streambed temperatures on a grid in winter and summer, building on prior monitoring during 1999 to 2002. Baseflow measured by both gauging and dye dilution generally increased with distance downstream, and stream and spring discharge varied seasonally, from minima in October to January to maxima in February to June. Thermal anomalies identified by probing occupied approximately 3% to 6% of the reach and typically coincided with visible springs or seeps. Locations of anomalies were similar to those identified in summer 2002, although some orifices disappeared and others emerged. Vertical groundwater fluxes calculated from probing tended to be less than net fluxes calculated from stream discharge, perhaps in part because the assumption of one‐dimensional, steady‐state flow in calculating point fluxes was simplistic. Maximum contaminant concentrations and fluxes decreased between 1999 to 2001 and 2011 to 2012 as a result of partial capture by an upgradient pump‐and‐treat system. Our findings confirm that springs in unlithified sediments can remain stationary within a few meters over decadal time scales, and seasonal variability in discharge can be greater than decadal variability.

Tripathi, Ganesh N.↗
DOE OSTI2021

Interannual variability of ecosystem iso/anisohydry is regulated by environmental dryness

Plants are characterized by the iso/anisohydry continuum depending on how they regulate leaf water potential (?L). However, how iso/anisohydry changes over time in response to year-to-year variations in environmental dryness and how such responses vary across different regions remains poorly characterized. We investigated how dryness, represented by aridity index, affects the interannual variability of ecosystem iso/anisohydry at the regional scale, estimated using satellite microwave vegetation optical depth (VOD) observations. This ecosystem-level analysis was further complemented with published field observations of ?L at the species level. We found different behaviors in the directionality and sensitivity of isohydricity (s) with respect to the interannual variation of dryness in different ecosystems. These behaviors can largely be differentiated by the average dryness of the ecosystem itself: in mesic ecosystems, s decreases in drier years, albeit at a higher rate; in xeric ecosystems, s increases in drier years, albeit at a lower rate. These results were supported by the species-level synthesis. Our study suggests that how plants adjust their water use across years – as revealed by their interannual variability in isohydricity – depends on the dryness of plants’ living environment. This finding advances our understanding of plant responses to drought at regional scales.

Wu, Genghong↗
DOE OSTI2026

Evolutionary constraints and climate variability jointly shape starch–sugar balance in woody plants

Nonstructural carbohydrates (NSC) buffer plants against carbon imbalances, yet their partitioning between storage and soluble pools remains elusive at global scales. Here, we compiled a dataset of starch to soluble sugar ratio (St : Su) for 308 woody species across 220 sites world-wide and introduce a dimensionless index that integrates storage and demand while minimizing methodological artifacts. St : Su was strongly associated with growth, identifying it as a key axis of carbon allocation. Foliage consistently exhibited lower St : Su than lignified organs, reflecting a division between transient and conservative pools. Conifers accumulated more starch in foliage but less in stems relative to angiosperms, while leaf habits and mycorrhizal associations further modulated organ-specific strategies. Contrary to expectation, foliar and root St : Su varied little among biomes, but stems exhibited higher ratios in tropical rainforests than in boreal or arid regions, reflecting differences in species composition and adaptive storage under disturbance. Phylogeny constrained stem storage, whereas climatic variability, rather than mean conditions, dominated allocation in leaves and roots. These findings establish St : Su as a robust functional trait linking allocation strategies, growth, and resilience, which can be used to improve vegetation model prediction of forest productivity and mortality under climate variability.

Li, Weibin [Lanzhou Univ. (China)] (ORCID:00000001↗
DOE OSTI2025

Pan-Pacific low-frequency modes of sea level and climate variability

Tide gauges provide a long observational record that can inform the nature of satellite-era basin-scale sea level trends. However, common signals must be extracted from geographically sparse records. Here, by applying low-frequency component analysis (LFCA) to tide gauge records and surface climate reconstructions, we isolate three coherent modes of Pacific Ocean variability that we ascribe to: a secular, greenhouse gas–driven climate change (LFC1); a nonlinear mode of variability with a reversal around 1980, potentially linked to aerosols (LFC2); and the Interdecadal Pacific Oscillation (LFC3). Although sea level trend patterns reflect the superimposed contribution of all modes, satellite-era trends are dominated by an increasing phase of LFC2: They are thus potentially unrepresentative of both longer-term historical patterns and those expected in the future.

Science & Technology - Other Topics↗
DOE OSTI2021

Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects

Decadal climate variability and change affects nearly every aspect of our world, including weather, agriculture, ecosystems, and the economy. Predicting its expression is thus of critical importance on multiple fronts. Power et al. review what is known about tropical Pacific decadal climate variability and change, the degree to which it can be simulated and predicted, and how we might improve our understanding of it. Here, more accurate projections will require longer and more detailed instrumental and paleoclimate records, improved climate models, and better data assimilation methods. —HJS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%–90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943–0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including “hypothetical proteins” was accurately predicted (F1 = 0.902 [0.898–0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876–0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

It is widely hoped that artificial intelligence will boost data-driven surrogate models in science and engineering. However, fundamental spatial aspects of AI surrogate models remain under-studied. We investigate the ability of neural-network surrogate models to predict solutions to PDEs under variable boundary values. We do not wish to retrain the model when the boundary values change but to make them inputs to the model and infer the solution of the PDE under those boundary conditions. Such a capability is essential to making AI-based surrogate models practically useful. While simple feedforward networks are used for one-dimensional (1D) Poisson equation, an encoder-decoder architecture with a tensor-product layer is developed for the two-dimensional Poisson equation posed on a rectangular domain. We show that it is indeed possible to infer solutions to PDEs from variable boundary data using neural networks in this relatively simple setting, and point to future directions.

Kashi, Aditya↗
DOE OSTI2023

Impedance Response Influenced by Variability in the Random Distribution of Physical Properties of Coated Materials in Two-Dimensional Space

Heterogeneous physical characteristics of a system featuring a single-layer film on a metallic surface have been explored via its impedance response. The Nyquist plot showed a distorted semicircle, indicative of the system's unique distribution characteristics. Utilizing a copula-based probability method, a two-dimensional deterministic impedance model was successfully integrated, accounting for spatial physical properties such as permittivity and electrical conductivity. This strategy enabled in-depth exploration and mechanistic quantification of a broad spectrum of properties. A quantitative understanding of impedance signal alterations, characterized by normally or log-normally correlated variables, was achieved through the variation in aspect ratio and characteristic frequency of the impedance spectra. Log-normally distributed electrical properties provided a superior representation of the distorted impedance spectra. As coefficient of variation (CV) values fluctuated, the aspect ratio and characteristic frequency showed heightened sensitivity to log-normal permittivity compared to log-normal electrical conductivity. Notably, a marked positive linear correlation between electrical properties resulted in an impedance response that approximated perfect semicircular spectra. The variability in the electrical properties' distribution was demonstrated by considering the correlation coefficient between electrical conductivity and the z-direction position. Furthermore, the highest aspect ratio of the impedance spectra was observed when the electrical conductivity was randomly distributed across the z-direction space.

36 MATERIALS SCIENCE↗
DOE OSTI2020

OpenStudio-Variability-Gem v1.0

This gem contains methods to introduce variability to extend core OpenStudio SDK. The term "variability" refers to changes in building systems efficiency, operation, or controls, which are non-routine events.

Li, Han↗
DOE OSTI2020

OpenStudio-Occupant-Variability-Gem v1.0

This gem contains methods to introduce occupant-related variability to extend core OpenStudio SDK. The goal of introducing the variability is to improve the assumptions related to stochastic occupant movement, occupant-related lighting and electric equipment, and thermostat set points schedules in traditional OpenStudio and EnergyPlus models.

Li, Han↗
DOE OSTI2024

Parallel Variable Population Multi-Objective Optimizer (pvpmoo) v1.0

This is a parallel variable population multi-objective optimizer with an adaptive unified differential evolution algorithm or a genetic algorithm. It can also be used for single objective optimization. Some features of this code include: 1) The population size varies from generation to generation to save the total # of objective function evaluations. 2) The population is uniformly distributed to a number of parallel processors for simultaneous objective function evaluation. 3) The objective function evaluation can be attained from an external simulation program with control variables in its input file and objectives calculated from its output files. 4) The optimizer includes an adaptive unified differential evolution algorithm and a real value genetic algorithm. The parameters in the unified differential evolution algorithm can be chosen to attain any mutation schemes in the published literature.

Qiang, Ji↗
DOE OSTI2023

Doppler Lidar Measurements of Wind Variability and LLJ Properties in Central Oklahoma during the August 2017 Land–Atmosphere Feedback Experiment

Abstract Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign [the Land–Atmosphere Feedback Experiment (LAFE)] studied LLJs over the central SGP site in Oklahoma and found nearly equal occurrences of the usual southerly jets and postfrontal northeasterly jets—typically rare during this season—for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the northeasterlies by more than 4 m s −1 on average, reflecting a significantly higher frequency of winds stronger than 12 m s −1 . The analysis of this dataset has been expanded to other SGP Doppler-lidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly wind nights, the winds blew stronger at the highest, westernmost sites by 2 m s −1 , whereas on the northeasterly flow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016–21 summertime data, revealed unusual wind and LLJ conditions. Temporal hodographs using hourly averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height. Significance Statement Doppler-lidar measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) were used to quantify the variability of winds and low-level jet (LLJ) properties between five sites of different land use and wind regimes across this area. Knowledge of wind and LLJ structure and dynamics is important for many applications, and strong southerly LLJ winds at night are an important resource for wind energy. The analysis of multiyear (2016–21) summertime LLJ parameters provided insight into the LLJ climatology in this part of the Great Plains.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Droplet Growth or Evaporation Does Not Buffer the Variability in Supersaturation in Clean Clouds

Abstract Water vapor supersaturation in clouds is a random variable that drives activation and growth of cloud droplets. The Pi Convection–Cloud Chamber generates a turbulent cloud with a microphysical steady state that can be varied from clean to polluted by adjusting the aerosol injection rate. The supersaturation distribution and its moments, e.g., mean and variance, are investigated for varying cloud microphysical conditions. High-speed and collocated Eulerian measurements of temperature and water vapor concentration are combined to obtain the temporally resolved supersaturation distribution. This allows quantification of the contributions of variances and covariances between water vapor and temperature. Results are consistent with expectations for a convection chamber, with strong correlation between water vapor and temperature; departures from ideal behavior can be explained as resulting from dry regions on the warm boundary, analogous to entrainment. The saturation ratio distribution is measured under conditions that show monotonic increase of liquid water content and decrease of mean droplet diameter with increasing aerosol injection rate. The change in liquid water content is proportional to the change in water vapor concentration between no-cloud and cloudy conditions. Variability in the supersaturation remains even after cloud droplets are formed, and no significant buffering is observed. Results are interpreted in terms of a cloud microphysical Damköhler number (Da), under conditions corresponding to , i.e., the slow-microphysics regime. This implies that clouds with very clean regions, such that is satisfied, will experience supersaturation fluctuations without them being buffered by cloud droplet growth. Significance Statement The saturation ratio (humidity) in clouds controls the growth rate and formation of cloud droplets. When air in a turbulent cloud mixes, the humidity varies in space and time throughout the cloud. This is important because it means cloud droplets experience different growth histories, thereby resulting in broader size distributions. It is often assumed that growth and evaporation of cloud droplets buffers out some of the humidity variations. Measuring these variations has been difficult, especially in the field. The purpose of this study is to measure the saturation ratio distribution in clouds with a range of conditions. We measure the in-cloud saturation ratio using a convection cloud chamber with clean to polluted cloud properties. We found in clouds with low concentrations of droplets that the variations in the saturation ratio are not suppressed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Representation of Modes of Variability in Six U.S. Climate Models

We compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Decadal Thermal Variability of the Upper Southern Ocean: Zonal Asymmetry

Abstract As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-twentieth century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (>8 years) variability extending to ∼700-m depth and is characterized by out-of-phase changes in the Pacific and Atlantic–Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic–Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal time scales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and preindustrial simulations of the Community Earth System Model version 1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the interdecadal Pacific oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea level pressure and anticyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic–Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

Meteorology & Atmospheric Sciences↗