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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The National Climate Database (NCDB): An Unbiased 100-Year Dataset for PV Modeling

In this study, we develop a statistical technique to downscale the future projection of solar irradiance for photovoltaics (PV) energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the downscaling method, a summary of the 100-year dataset from 2001-2100, and future extension of this research.

bias correction↗
DOE OSTI2025

ISMIP6-based Antarctic projections to 2100: simulations with the BISICLES ice sheet model

The contribution of the Antarctic Ice Sheet is one of the most uncertain components of sea level rise to 2100. Ice sheet models are the primary tool for projecting future sea level contribution from continental ice sheets. The Ice Sheet Model Intercomparison for the Coupled Model Intercomparison Phase 6 (ISMIP6) provided projections of the ice sheet contribution to sea level over the 21st century, quantifying uncertainty due to ice sheet model, climate model, emission scenario, and uncertain parameters. We present simulations following the ISMIP6 framework with the BISICLES ice sheet model and new experiments extending the ISMIP6 protocol to more comprehensively sample uncertainties in future climate, ice shelf sensitivity to ocean melting, and their interactions. These results contributed to the land ice projections of Edwards et al. (2021), which formed the basis of sea level projections for the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6). Our experiments show the important interplay between surface mass balance processes and ocean-driven melt in determining Antarctic sea level contribution. Under higher-warming scenarios, high accumulation offsets more ocean-driven mass loss when sensitivity to ocean-driven melt is low. Conversely, we show that when sensitivity to ocean warming is high, ocean melting drives increased mass loss despite high accumulation. Overall, we simulate a sea level contribution range across our experiments from 2 to 178 mm. Finally, we show that collapse of ice shelves due to surface warming increases sea level contribution by 25 mm relative to the no-collapse experiments, for both moderate and high sensitivity of ice shelf melting to ocean forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

HD ADOPT: Heavy-Duty Vehicle Choice Model Documentation

HD ADOPT is a logit consumer vehicle choice and stock model that analyzes the Class 8 tractor market. The model projects future technology shares, fuel consumption, and greenhouse gas (GHG) emissions under input assumptions of technology progress, energy prices, and policies. ADOPT is distinguished from other vehicle choice models through inclusion of non-linear and heterogenous consumer preferences and characterization of the full range of market options rather than use of composite vehicles. In addition, ADOPT has integrated vehicle simulation capabilities that enable performance assessment and optimization of endogenous technology evolution. Optionally, the model is able to adjust this evolution to enforce compliance with fuel economy and GHG emissions regulations. Primary results include projection of technology shares and future in-use fleet energy demand, petroleum consumption, and GHG emissions. This enables analysis and comparison of future scenarios of technology improvements, economic conditions, and national policies. Recent new features for the HD modeling also enable examination of different on-board hydrogen fuel storage technologies from the lens of consumer preferences for vehicle cost and range. This report documents current ADOPT capabilities and methodologies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Strategic Energy Plan, Town of Cranberry Isles

This report details the strategic energy plan for the Town of Cranberry Isles, Maine. This project was spearheaded by the Cranberry Isles Community Solar Association (CICSA), and supported by Island Institute and Sandia National Laboratories, in partnership with the National Laboratory of the Rockies and Versant Power. The plan details the energy vision, goals, and focus areas for the community, provides technical analysis of the current energy system, and outlines potential projects to support community energy. Throughout the energy planning process, the project team engaged with members of the community to gather input and ideas to shape the plan. The project team and Versant also completed an extensive data collection to better inform the project. Sandia National Laboratories completed baseline technical assessments and feasibility studies to better understand potential feasible options for future projects. These potential projects, high-level costs, and next steps are a key output of this report.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

How Frequent Will the Rarest Daily Rainfall Records of Hurricane Ida’s Remnants Be in the Future?

Abstract Gaining continued insights into the impact of global warming on the occurrence of hurricane-associated intense record downpours is essential for building climate resilient communities. This study investigates projected future changes in extreme rainfall over the Northeast United States, as represented by extreme daily amounts during Hurricane Ida in 2021. We used historical control simulations of Weather Research and Forecasting (WRF) Model generated from 40 years of weather events (1980–2014, 12 km) forced by the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis. These simulations are thermodynamically modified (2060–2100) via an imposed warming for the high-emission scenario of shared socioeconomic pathway (SSP585) from a range of general circulation models. Ground observations from the Global Historical Climatology Network (1950–2014) and WRF simulations (historical, 1980–2014, and future, 2060–2100) are integrated into a nonstationary generalized extreme value (GEV) framework to assess the frequency of Ida’s heaviest daily rain rates under the SSP585 scenario. Results show that Ida’s daily maximum rainfall recorded at different observation locations was higher than the single highest September daily maximum observed (1950–2014) for 5 out of 17 stations (∼30% of the stations). Ida-like extreme daily rain rates are projected to be, on average, more than 2 times more likely to occur at the end of the century in the simulations (with some regions as high as 5 times). This work demonstrates that integrating a high-resolution atmospheric model’s present-day and thermodynamically modified future simulations along with ground observations, within a nonstationary statistical framework, is crucial for understanding changing characteristics of extreme weather events. Significance Statement Daily scale extreme precipitation is expected to become more frequent and severe, as evidenced by observations and model simulations. While it is important to investigate how these intensifying heavy rainfall events affect current engineering standards, fewer studies have contextualized how warming impacts the most extreme rainfall from a single storm event relative to historical heavy downpours. In this study, we focused on the daily extreme rainfall associated with the extratropical transition of Hurricane Ida (2021), particularly over the northeastern United States—some of which exceeded the commonly used hydrologic design criteria for a 100-yr storm. Using a high-resolution atmospheric model simulation, we investigated how continued warming may influence the frequency of such daily rain rates. Under a high-emission scenario, these events are projected to become up to 5 times more likely at the end of the twenty-first century.

Dollan, Ishrat J↗
DOE OSTI2024

Reliability and resilience of environmental flows under uncertainty: reconsidering water year types and inconsistent flow requirements in California

Abstract Environmental water allocation in California is a complex legal process involving various government agencies and stakeholders. E-flow requirements can be based on annual runoff typologies called water year types (WYTs), which dictate water volume, timing, and duration. In this study, we examined hydropower licensing documents of the major water and power projects in the Central Sierra Nevada to catalog e-flow requirements by WYT. In this study case, we identify how WYT classification systems and categories vary across and within different basins. Additionally, we assessed the impacts of climate change on hydrology, the frequency of WYTs identified, and the reliability and resilience of e-flows using future projections (2031–2060) of 10 Global Circulation Models (GCMs). We then propose a potential adaptation strategy using a 30 year moving percentiles approach to recalculate WYTs. We identified eight WYT classifications systems were identified, and their WYT distributions statistically significantly changes across all GCMs, even though most GCMs indicate no statistically significant change in hydrology. Disparities in future impacts are observed among and within hydropower projects, with some river reaches showing negative impacts on reliability and resilience. The adaptation strategy can generally boost resilience and improve reliability, but simply updating existing WYT thresholds without flexible regulatory frameworks reconsidering WYTs and e-flows thresholds, may not yield substantial improvements. Challenges in managing e-flows in California within regulatory and hydroclimatic contexts are intricate due to the lack of standardized approaches, leading to inconsistencies and potential conflicts among stakeholders, that will likely be exacerbated by climate change. Thus, we emphasize that targeted, site-specific, and adaptive management strategies are crucial, besides the need for a harmonized and consistent approach to defining and applying WYT categories and methods and/or e-flow assessments.

Environmental Sciences & Ecology↗
DOE OSTI2024

Tropical Indian Ocean drives Hadley circulation change in a warming climate

ABSTRACT The weakening and poleward expansion of the Hadley circulation (HC) are considered robust responses of atmospheric meridional circulation to anthropogenic warming. Climate impacts arising from these changes enhance drought conditions and reduce food production in the affected regions. Therefore, understanding the mechanisms of HC changes is critical to anticipating the resultant climate risks. First, we demonstrate that robust future HC changes in boreal winter, and the uncertainty in their future projections, are both largely related to sea surface temperature (SST) warming. Next, we investigate the impact of anthropogenic regional ocean warming on the future HC. Accordingly, we conduct a large ensemble of individual ocean basin perturbation experiments at 1.5°C, 2°C, and 3°C warming thresholds (as in the Paris Agreement). These experiments highlight (i) the leading role of tropical Indian Ocean warming in HC changes and (ii) inter-model differences in tropical Pacific warming as a source of uncertainty in HC projections.

Sun, Yong (ORCID:0000000210876060)↗
DOE OSTI2025

Enhanced Simulation of Atmospheric Blocking in a High‐Resolution Earth System Model: Projected Changes and Implications for Extreme Weather Events

Atmospheric blocking is closely linked to the occurrence of extreme weather events. However, low-resolution Earth system models often underestimate the frequency of blocking, undermining confidence in future projections. Here, in this study, we use the high-resolution Community Earth System Model (CESM-HR; 25 km atm and 10 km ocean) to show that CESM-HR reduces biases in atmospheric blocking for both winter and summer, particularly for events lasting longer than 10 days. This improvement is partly due to reduced sea surface temperature biases at higher resolution. Additionally, applying a bias correction to the 500 hPa geopotential height further enhances blocking frequency simulations, highlighting the crucial role of the mean state. Under the Representative Concentration Pathway 8.5 scenario, CESM-HR projects a decrease in wintertime blocking over regions such as the Euro-Atlantic and Chukchi-Alaska, consistent with previous studies. In contrast, summer blocking is expected to become more frequent and persistent, driven by weakened zonal winds. The blocking center shifts from historical locations over Scandinavia and eastern Russia to central Eurasia, significantly increasing blocking over the Ural region. Summer blocking frequency over the Scandinavia-Ural region may eventually surpass historical winter blocking over the Euro-Atlantic. This increase in summer blocking could exacerbate summer heatwaves in a warming climate, making severe heatwaves, like those observed recently, more common in the future.

Atmospheric blocking↗
DOE OSTI2024

Recalibration of missing low-frequency variability and trends in the North Atlantic Oscillation

Abstract Multi-decadal trends in the wintertime North Atlantic Oscillation (NAO) are under-represented by coupled general circulation models (CGCMs), consistent with a lack of autocorrelation in their NAO index series. This study proposes and tests two simple “reddening” approaches for correcting this problem in simulated indices based on simple one parameter short-term (AR; Auto-Regressive order 1) and long-term (FD; Fractional-Difference) time series filters. Using CGCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the FD filter successfully improves the autocorrelation structure of the NAO, and in turn the simulation of extreme trends, while the AR filter is less successful. The 1963–1993 NAO trend is the maximum 31-year trend in the historical period. Raw CGCMs underestimate the likelihood of this trend by a factor of ten but this discrepancy is corrected after reddening. CMIP6 future projections show that long-term (2024–2094) NAO ensemble mean trends systematically increase with the magnitude of radiative forcing: -2.4 to 3.5 hPa/century for low-to-high forcing after reddening (more than double the range using raw output). The related likelihood of future maximum 31year trends comparable to 1963–1993 ranges from 3 to 7% whereas none of these CMIP6 projections simulate this without reddening. Near-term projections of the next 31 years (2024–2054) are less sensitive than long term trends to the future scenario, showing weak-to-no forced trend. However, reddening increases the ensemble range by 74% (to +/-1 standard deviation/decade), which could increase/decrease regional climate change signals in the Northern Hemisphere by magnitudes that are underestimated when using raw CGCM output.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

How do drought and heat affect the response of soybean seed yield to elevated O 3 ? An analysis of 15 seasons of free–air O 3 concentration enrichment studies

The coincidence of rising ozone concentrations ([O 3 ]), increasing global temperatures, and drought episodes is expected to become more intense and frequent in the future. A better understanding of the responses of crop yield to elevated [O 3 ] under different levels of drought and high temperature stress is, therefore, critical for projecting future food production potential. Using a 15-year open-air field experiment in central Illinois, we assessed the impacts of elevated [O 3 ] coupled with variation in growing season temperature and water availability on soybean seed yield. Thirteen soybean cultivars were exposed to a wide range of season-long elevated [O 3 ] in the field using free-air O 3 concentration enrichment. Elevated [O3] treatments reduced soybean seed yield from as little as 5.3% in 2005 to 35.2% in 2010. Although cultivars differed in yield response to elevated [O 3 ] (R), ranging from 17.5% to –76.4%, there was a significant negative correlation between R and O 3 dosage. Soybean cultivars showed greater seed yield losses to elevated [O 3 ] when grown at drier or hotter conditions compared to wetter or cooler years, because the hotter and drier conditions were associated with greater O 3 treatment. However, year-to-year variation in weather conditions did not influence the sensitivity of soybean seed yield to a given increase in [O 3 ]. Collectively, this study quantitatively demonstrates that, although drought conditions or warmer temperatures led to greater O 3 treatment concentrations and O 3 -induced seed yield reduction, drought and temperature stress did not alter soybean's sensitivity to O 3 . Our results have important implications for modeling the effects of rising O 3 pollution on crops and suggest that altering irrigation practices to mitigate O 3 stress may not be effective in reducing crop sensitivity to O 3 .

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Evaluation of CMIP6 Streamflow in the Arctic

Earth system models (ESMs) serve as the primary basis for projecting future streamflow changes, but they are biased in terms of the their ability to reproduce historical observations of streamflow. Rigorous downscaling and bias-correction procedures, which are time-consuming and introduce uncertainties, are commonly used to address these biases in streamflow or runoff. However, the applicable limits of streamflow projections remain largely untested through direct comparisons with observations. In this work, we compare historical streamflow time series observations with a suite of Coupled Model Intercomparison Project phase 6 (CMIP6) ESMs to assess the space and time scales at which each ESM represents historic streamflow observations. We focus our analysis on streamflow across the Arctic given the higher degree of warming experienced over this region, underscoring its importance for understanding the future changes. A series of metrics—volume, seasonality, extreme capturing streamflow variability, drought, and flooding is tested to assess ESM simulation skill for total events and overall distributions. Our results indicate that although improvements are necessary in ESM runoff and streamflow projections, the use of temporal averaging and targeting the best-fit model for comparison to observations when a suite of ESMs is considered can provide robust streamflow projections. Moreover, this work provides a basis for contextualizing the limits of ESM applicability for studies aiming to assess streamflow in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Response of hypoxia to future climate change is sensitive to methodological assumptions

Climate-induced changes in hypoxia are among the most serious threats facing estuaries, which are among the most productive ecosystems on Earth. Future projections of estuarine hypoxia typically involve long-term multi-decadal continuous simulations or more computationally efficient time slice and delta methods that are restricted to short historical and future periods. We make a first comparison of these three methods by applying a linked terrestrial–estuarine model to the Chesapeake Bay, a large coastal-plain estuary in the eastern United States. Results show that the time slice approach accurately captures the behavior of the continuous approach, indicating a minimal impact of model memory. However, increases in mean annual hypoxic volume by the mid-twenty-first century simulated by the delta approach (+ 19%) are approximately twice as large as the time slice and continuous experiments (+ 9% and + 11%, respectively), indicating an important impact of changes in climate variability. Our findings suggest that system memory and projected changes in climate variability, as well as simulation length and natural variability of system hypoxia, should be considered when deciding to apply the more computationally efficient delta and time slice methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Exploring the role of clouds in offshore wind potential off the US West Coast in a changing climate

To meet US goals of deploying additional wind energy as part of the decarbonization strategy, wind plants are being planned for the deep water offshore the western US. The wind flow in that region is complex due to the proximity to the coast, cold water upwelling, and persistent stratiform clouds that interact with radiation in ways that have the potential to destabilize the atmosphere. That flow has the potential to change with a changing climate. To address these issues, we assess the flow and the clouds in that region using downscaled climate model data, under both historic climate (1975–2005) and projected future (2025–2055) conditions. We note that the climate simulations agree fairly well with the cloud patterns observed by satellite data in the nearshore and offshore regions. We then assess the projected changes in clouds, wind speed, and other important variables, noting that our simulations project that the predominant north/northwesterly low-level jet is expected to strengthen and clouds are likely to be commensurately enhanced, although projected changes are within about 10% of current conditions. Our examination of the dynamics associated with the changes in the climate simulations provides confidence in the dynamical consistency of these projected changes.

17 WIND ENERGY↗
DOE OSTI2026

Los Alamos National Laboratory R&D Intern

This report details the responsibilities, outcomes, and project details of a summer R&D internship at Los Alamos National Laboratory (LANL). LANL is a multidisciplinary laboratory that focuses on current cutting-edge research in many fields such as national security, engineering, materials science, computational modeling, and advanced manufacturing. The goal of the internship project was to work with lab engineers and resources to develop an energy absorbing structure for high-velocity impact applications. The successful development of this technology and methodology would not only positively impact future project funding but also contribute to the laboratory's commitment to solve national security challenges through simultaneous excellence. Such devices would also support efforts surrounding the research and development of energy absorbing structures and would provide new vital information backed by experimentation. Different computational and modeling methods were used to design these structures, in addition to qualitative background information provided by past literature. The resultant designs were successfully tested, and the test results were successfully quantified. From these results, new computational methods were developed through python programming and modeling to predict ideal materialistic properties for these structures given a sufficiently defined application.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Cully Community Solar

This report summarizes the outcomes, accomplishments, challenges, and lessons learned from Verde’s Cully Community Solar project, funded by the U.S. Department of Energy’s Solar Energy Technologies Office. The project focused on advancing community solar development through engineering, permitting, interconnection, ecological site design, workforce development, and community engagement. During the project period, Verde and its partners successfully developed and commissioned a 1.25 MW ground-mounted community solar system while also advancing planning for additional community solar capacity and developing approaches that can inform future projects in complex urban environments.

14 SOLAR ENERGY↗
DOE OSTI2024

Tethys: A Spatiotemporal Downscaling Model for Global Water Demand

Humans use water for many important tasks, such as drinking, growing food, and cooling power plants. Since future water demands depend on complex global interactions between economic sectors (e.g., demand for wheat in one country causing demand for water to grow that wheat in another country), it is often modeled at coarse spatial and temporal scales as part of models that account for complex, multi-sector system dynamics. However, models that project future water availability typically simulate physical processes at much finer scales. Tethys enables integration between these kinds of models by downscaling region-scale water demand projections using sector-specific proxies and formulas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Coupled Climate Simulations With E3SM‐MMF

Simulations of the recent historical period from 1950 to 2014 are conducted with E3SM‐MMF, which uses an embedded 2D cloud resolving model that runs efficiently on GPUs in place of traditional parameterizations for cloud and turbulence. Analysis of the climate and variability reveal several aspects where E3SM‐MMF produces smaller biases compared to E3SMv2, including better agreement with the observed evolution of global mean surface temperature, although the representation of ENSO is too weak and fast. Three idealized abrupt CO 2 experiments were also conducted to assess climate sensitivity and feedbacks. These yield three estimates of effective climate sensitivity (4.38, 5.21, and 6.06 K), with a corresponding spread in the shortwave cloud feedbacks. These estimates are on the higher end of sensitivity estimates from CMIP ensembles, and the spread indicates substantial state‐dependent feedbacks. These results demonstrate how multiscale modeling framework (MMF) models can be used for climate relevant experiments and projections by leveraging modern GPU enabled computational platforms. The unique qualities of E3SM‐MMF shown in previous literature are largely still present, but various instances of reduced biases suggest that MMF models have utility in improving future projections.

E3SM↗