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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 73 records · Page 4
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DOE OSTI2026

Historical and Future Global Irrigation Energy Consumption by Fuel and Region

Irrigation energy use is a significant component of agricultural production costs, contributing directly to the energy and emissions intensity of crop production and ultimately to food prices. Understanding the existing structure of irrigation energy consumption help achieve food-energy-water security and environmental goals. We present a comprehensive global data set detailing country-level irrigation energy consumption, emphasizing the comparative use of electric, diesel, and emerging solar pumps. To our knowledge, no such data set exists. We draw from a literature review to develop a logistic transformed regression model to estimate the shares of fuel sources for irrigation across countries over historical years to construct a global data set of country-level irrigation energy consumption by multiple fuel sources. Additionally, we compare our estimates of irrigation energy use with agricultural energy use as reported by the International Energy Agency and other external sources. We then use this data to project future irrigation energy use with the Global Change Analysis Model, which is a multisector dynamics model, to showcase the usage of this data set. Projections under the reference scenario show a global shift in fuel types for irrigation pumping, while patterns vary across regions, with India and Pakistan leading in solar-powered irrigation growth and countries like the USA and China continuing to rely primarily on grid electricity. This data set provides a resource to understand the role of irrigation fuel choices within the broader energy sector, as well as the connected agricultural, land use, and water sectors under alternative future scenarios, enabling informed decision making toward efficient agricultural practices.

Global Change Analysis Model (GCAM)↗
DOE OSTI2025

Secondary Organic Aerosol from OH Oxidation of Acyclic Terpenes Is More Viscous and Less Volatile than That of Their Cyclic Analogs

Biogenic volatile organic compounds (BVOCs), a dominant source of secondary organic aerosol (SOA) globally, exhibit emission rates and composition that are plant species-specific and vary with environmental stressors. A common outcome of plant stress is increased emissions of acyclic terpenes. The paucity of information about acyclic terpene SOA chemistry contributes to uncertainties in predictions of SOA global loadings and impacts on Earth’s radiative budget, particularly in a changing climate where acyclic terpene emissions could become more prominent. This study compared properties of SOA derived from OH oxidation of acyclic and cyclic monoterpenes (ß-ocimene, a-pinene) and sesquiterpenes (ß-farnesene, ß-caryophyllene). Single particle mass spectrometry was used for assessing shape, density, and evaporation kinetics of size-selected SOA particles, and nanospray desorption electrospray ionization high-resolution mass spectrometry (nano-DESI-HRMS) was used to measure molecular composition of SOA. Acyclic terpene SOA exhibited higher viscosity and lower volatility compared to cyclic terpene SOA, and had a greater volume fraction remaining (VFR) after ~24 hours of evaporation - approximately 1.3-1.6 times higher VFR than that of cyclic terpene SOA. Additionally, HRMS analysis revealed greater chemical diversity and higher fractions of extremely low volatility compounds (56-62% ELVOC/LVOC) in acyclic terpene SOA compared to cyclic counterparts (25-37% ELVOC/LVOC). Our findings highlight the potential importance of accounting for acyclic terpene aerosol chemistry under conditions of plant stress to improve predictions of SOA loadings and impacts.

SOA physical properties↗
DOE OSTI2025

Report on the Atmospheric Temperature Changes and their Drivers (ATC) Activity 2025 Spring Meeting

The Atmospheric Temperature Change and their Drivers (ATC) Activity brings together experts interested in improving understanding of atmospheric temperature variability and trends and their representation in climate data records. ATC pursues this goal by fostering intercomparisons of atmospheric temperature datasets, providing and improving uncertainty information for climate data records, comparing observations with model simulations, assessing atmospheric temperature trends and their drivers, and documenting their efforts in review papers and assessment reports. The ATC activity convened at the Wegener Center for Climate and Global Change at the University of Graz in Graz, Austria over April 23 – 25. The purpose of the meeting was to provide updates on research and datasets related to atmospheric temperature change and variability, to identify areas that need further research, and to coordinate ongoing and future collaborations. Meeting themes included theoretical and simulated controls on atmospheric temperature, the development of new and improved atmospheric temperature datasets, and analysis of atmospheric temperature variability and trends. 18 activity members attended the meeting including 12 in-person attendees and 6 remote attendees. Four new early career activity members attended with support from APARC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

BSM Sensitivity Analysis and Meta-Modeling Next Steps [Slides]

We performed a global sensitivity analysis of the Bioenergy Scenario Model (BSM), using Elementary Effects analysis to identify potentially influential factors, with a focus on sustainable aviation fuel (SAF) production. The process was iterative and informed key model changes and updates over the course of the study in addition to identifying the key model inputs that affect the cumulative production of SAF from the present through 2050. More work will be performed to gain deeper insight into the sensitivity of individual pathways as part of our larger efforts to develop a reduced-form version of the BSM.

09 BIOMASS FUELS↗
DOE OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

LiG Metrology, Correlated Error, and the Integrity of the Global Surface Air-Temperature Record

The published 95% uncertainty of the global surface air-temperature anomaly (GSATA) record through 1980 is impossibly less than the 2σ = ±0.25 °C lower limit of laboratory resolution of 1 °C/division liquid-in-glass (LiG) thermometers. The ~0.7 °C/century Joule-drift of lead- and soft-glass thermometer bulbs renders unreliable the entire historical air-temperature record through the 19th century. A circa 1900 Baudin meteorological spirit thermometer bulb exhibited intense Pb X-ray emission lines (10.55, 12.66, and 14.76 keV). Uncorrected LiG thermometer non-linearity leaves 1σ = ±0.27 °C uncertainty in land-surface air temperatures prior to 1981. The 2σ = ±0.43 °C from LiG resolution and non-linearity obscures most of the 20th century GSATA trend. Systematic sensor-measurement errors are highly pair-wise correlated, possibly across hundreds of km. Non-normal distributions of bucket and engine-intake difference SSTs disconfirm the assumption of random measurement error. Semivariogram analysis of ship SST measurements yields half the error difference mean, ±½Δε 1,2, not the error mean. Transfer-function adjustment following a change of land station air-temperature sensor eliminates measurement independence and forward-propagates the antecedent uncertainty. LiG resolution limits, non-linearity, and sensor field calibrations yield GSATA mean ±2σ RMS uncertainties of, 1900–1945, ±1.7 °C; 1946–1980, ±2.1 °C; 1981–2004, ±2.0 °C; and 2005–2010, ±1.6 °C. Finally, the 20th century (1900–1999) GSATA, 0.74 ± 1.94 °C, does not convey any information about rate or magnitude of temperature change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Tracing U.S. fuel life-cycle greenhouse gas emissions in a multi-sector dynamics model using LC-GCAM

Model-based analysis of fuel pathways is essential for informing energy and environmental policy. Two major model types are typically used: multi-sector dynamics models, which capture the broader energy-economy, such as GCAM (Global Change Analysis Model), and life cycle assessment models, such as GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation). Each has distinct strengths and limitations, and recent studies increasingly adopt hybrid approaches to harness the advantages of both. However, such integration is often time-consuming and complicated by inconsistencies in system boundaries and technology definitions. We present LC-GCAM, a new tool that enables estimation of life-cycle greenhouse gas emissions and primary energy use for any fuel pathway represented in GCAM. We apply LC-GCAM to 300 scenarios designed to explore key uncertainties affecting the life-cycle performance of future fuel options in the U.S. freight sector. To evaluate LC-GCAM, we compare its results with those from GREET for nine fuel types in a 2030 reference scenario. When input assumptions are modestly aligned, LC-GCAM and GREET estimates typically agree within 10% (absolute sum-based mean absolute percentage error). LC-GCAM offers a flexible and efficient approach to generating life-cycle metrics within an integrated modeling framework, supporting robust policy analysis across a wide range of interacting energy system uncertainties.

Wolfram, Paul↗
DOE OSTI2022

Renewable Energy and Efficiency Technologies in Scenarios of U.S. Decarbonization in Two Types of Models: Comparison of GCAM Modeling and Sector-Specific Modeling

Energy system projections from analytic models inform actions ranging from short-term and local decisions, such as technology and infrastructure deployment, to global and long-term negotiations and targets. Computational limits require the designers of these models to trade off between coverage and resolution. Some models, such as the Global Change Analysis Model (GCAM), represent all energy sources and uses but at a relatively coarse level of resolution. GCAM balances global supply and demand of all energy carriers by endogenously projecting prices for energy sources and costs of greenhouse gas mitigation while capturing interlinkages between the energy system, water, agriculture and land use, the economy, and the climate. This global model was used to frame the Long-Term Strategy released by the White House in 2021 and has been used to inform national and global economy-wide decarbonization discussions and strategy development for decades. Other models instead focus on a portion of the energy sector with greater detail and resolution. The Regional Energy Deployment System (ReEDS) electricity-sector model, for example, projects capacity expansion with an emphasis on integration of variable renewable energy into the grid of the future. The Transportation Energy and Mobility Pathway Options (TEMPO) transportation-sector model enables analysis of household choices in adoption, charging, and use of electric vehicles. The Scout buildings-sector model supports detailed consideration of the policies and markets that can accelerate the adoption of energy conservation measures in buildings. Such sector-specific models are instrumental in informing technology research, sectoral planning strategies, and sector-specific aspects of greenhouse gas (GHG) mitigation strategies in the United States. These global and sector-specific modeling approaches can complement each other. The global approach ensures consistent, endogenous energy pricing and resource allocation, which can substantially diverge from current conditions in transformative scenarios, while the sector-specific approach facilitates representation of granular details across spatial, temporal, technological, and market dimensions that enable exploration of particular interactions and trade-offs. This report presents the results of recent work to explore the differences and tradeoffs between these approaches by comparing GCAM with the sector-specific ReEDS, TEMPO, and Scout models. The report compares both model structures and results, and discusses their potential relevance and applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Local Interface Effects Modulate Global Charge Order and Optical Properties of 1T–TaS2/1H–WSe2 Heterostructures

1T-TaS2 is a layered charge density wave (CDW) crystal exhibiting sharp phase transitions and associated resistance changes. These resistance steps could be exploited for information storage, underscoring the importance of controlling and tuning the CDW states. Given the importance of out-of-plane interactions in 1T-TaS2, modulating interlayer interactions by heterostructuring is a promising method for tailoring CDW phase transitions. In this work, we investigate the optical and electronic properties of heterostructures comprising 1T-TaS2 and monolayer 1H-WSe2. By systematically varying the thickness of 1T-TaS2 and its azimuthal alignment with 1H-WSe2, we find that intrinsic moiré strain and interfacial charge transfer introduce CDW disorder in 1T-TaS2 and modify the CDW ordering temperature. Furthermore, our studies reveal that the interlayer alignment impacts the exciton dynamics in 1H-WSe2, indicating that heterostructuring can concurrently tailor the electronic phases in 1T-TaS2 and the optical properties of 1H-WSe2. This work presents a promising approach for engineering the optoelectronic behavior of heterostructures that integrate CDW materials and semiconductors.

charge density wave↗
DOE OSTI2023

Comparing land surface temperature and mean radiant temperature for urban heat mapping in Philadelphia

Many cities are experiencing more frequent extreme heat during hot summers. With the rise of global temperature, the thermal comfort in urban areas become even worse. Quantitative information of the spatial distributions of urban heat has become increasingly important for resilience and adaptation to climate change in cities. This study compares satellite-derived land surface temperature (LST) and urban microclimate modeling-based mean radiant temperature (Tmrt) for mapping the urban heat distributions in Philadelphia, Pennsylvania, USA. Here, the LST was estimated based on Landsat 8 thermal imagery with a spatial resolution of around 100 m, while the Tmrt was simulated based on high resolution LiDAR and national aerial imagery program multispectral aerial imageries with a spatial resolution of 1 m. Result shows that both LST and Tmrt show a similar general pattern of the urban heat across the study area, while the Tmrt presents much more details of the heat variations street by street and neighborhood by neighborhood. The LST tends to have a stronger relationship with the Tmrt on building roofs, which are usually not the place for human activities. This studyprovides evidence for choosing more appropriate metrics in urban heat-related studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Interconnected Risks in Electricity Systems: Understanding Research Challenges, Needs, and Partnerships

The U.S. federal government has a potential role in mitigating the most poorly addressed risks across electricity markets and interdependent systems. Electricity system risks stem from extreme acute shocks (e.g., cyberattacks, extreme weather, and supply chain disruptions) and chronic stressors (e.g., global economic competition, uncertainties surrounding emerging technologies, climate change, and related conflicts). These acute shocks and chronic stressors can result in dire consequences for society, underscoring the need for better information for decision makers in both public and private sectors. Best practices and a shared understanding of risk management and risk quantification must be rapidly developed and disseminated. Informed by a quantitative treatment of risk to electrical infrastructure and interdependent systems, decision makers can more prudently and efficiently prioritize mitigation initiatives to maximize benefits to government, businesses, and the public.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs With Physics‐Informed Neural Networks

Accurate representation of pumping wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) are emerging as a promising alternative to numerical models for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells when using a source/sink term, particularly during the early stages after pumping begins. We address this problem by introducing WellPINN, a workflow in which an initially trained PINN infers fluid pressure across the entire reservoir domain using a large equivalent well radius. This initial PINN solution is then locally refined around the well by a set of subdomain PINNs that are trained for smaller equivalent well radii. Continuity across these subdomain interfaces as well as at the initial condition is ensured by hard-constraining each PINN on its subdomain boundary. Our results demonstrate WellPINN as the first workflow of its kind to focus on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper are openly available at https://doi.org/10.20350/DIGITALCSIC/17260.

58 GEOSCIENCES↗
DOE OSTI2023

Downscaling and bias-correction contribute considerable uncertainty to local climate projections in CMIP6

Abstract Efforts to diagnose the risks of a changing climate often rely on downscaled and bias-corrected climate information, making it important to understand the uncertainties and potential biases of this approach. Here, we perform a variance decomposition to partition uncertainty in global climate projections and quantify the relative importance of downscaling and bias-correction. We analyze simple climate metrics such as annual temperature and precipitation averages, as well as several indices of climate extremes. We find that downscaling and bias-correction often contribute substantial uncertainty to local decision-relevant climate outcomes, though our results are strongly heterogeneous across space, time, and climate metrics. Our results can provide guidance to impact modelers and decision-makers regarding the uncertainties associated with downscaling and bias-correction when performing local-scale analyses, as neglecting to account for these uncertainties may risk overconfidence relative to the full range of possible climate futures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Biocrust Mosses and Cyanobacteria Exhibit Distinct Carbon Uptake Responses to Variations in Precipitation Amount and Frequency

ABSTRACT Dryland organisms exhibit varied responses to changes in precipitation, including event size, frequency, and soil moisture duration, influencing carbon uptake and reserve management strategies. This principle, central to the pulse‐reserve paradigm, has not been thoroughly evaluated in biological soil crusts (biocrusts), essential primary producers on dryland surfaces. We conducted two experiments to investigate carbon uptake in biocrusts under different precipitation regimes. In the first, we applied a gradient of watering amounts to biocrusts dominated by moss or cyanobacteria, hypothesising distinct pulse‐response strategies. The second experiment extended watering treatments over three months, varying pulse size and frequency. Our results revealed distinct carbon uptake patterns: moss crusts exhibited increased CO 2 uptake with larger, less frequent watering events, whereas cyanobacteria crusts maintained similar carbon uptake across all event sizes. These findings suggest divergent pulse‐response strategies across biocrust types, with implications for modelling dryland carbon dynamics and informing land management under changing precipitation regimes.

Young, Kristina E. [USDA‐ARS Jornada Experimental ↗
DOE OSTI2023

National CO 2 budgets (2015–2020) inferred from atmospheric CO 2 observations in support of the global stocktake

Accurate accounting of emissions and removals of CO 2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (X CO 2 ) retrievals (ACOS v10), in situ CO 2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1° x 1° gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr -1 (0.90–1.25 Pg C yr -1 ). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A U.S. Scientific Community Vision for Sustained Earth Observations of Greenhouse Gases to Support Local to Global Action

Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.

Parazoo, Nicholas C. [California Institute of Tech↗
DOE OSTI2020

Climate Change Science Review 2018 and Associated Social and Economic Impacts

Climate change and its impacts on average temperature, water supply, agriculture, coastal flooding, biodiversity, social and economic stability, human migration, and overall global stability is one of the leading threats emerging for humanity, and is likely to increase as a threat for decades to come. This report represents a snapshot of climate change data, information, and understanding, as of 2018, and can serve as a kind of benchmark for changes going forward in time. This report covers temperature change and heat effects, atmospheric and ocean circulation, freshwater supply changes, sea level rise and flooding, extreme climate events, oceanic deoxygenation, land degredation, social and economic changes, migration, and food. Data and information on all these issues represent a compelling body of knowledge supporting the scientific hypothesis describing climate change, and an important mile marker in our effort to track the unfolding nature of the problem. A slide presentation that summarizes the content of this report is in Appendix A and can serve as an executive summary for the report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology

Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1–2 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances and the impact of phenology changes on ecosystem productivity and global carbon exchange.

Weber, Maridee A.↗