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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A hierarchical evaluation framework for assessing climate simulations relevant to the energy-water-land nexus (Final Report)

The overarching goal was to construct a hierarchy of new and well-tested metrics and analysis tools that support both fundamental and use-inspired research. Motivating this goal was a convergence of needs from climate scientists and stakeholders alike for a systematic, robust framework of model evaluation and diagnosis to provide scientific insights, inform model development, support best practices for the use of climate model outputs, and facilitate communication of climate information in the evolving landscapes of multi-model, multi-resolution, and large ensemble simulations that generate terabytes of data for any single climate run. Steps toward attaining these goals benefited from expertise and established capability of the project team, which included leadership of the North American Regional Climate Change Assessment Program (NARCCAP) and the Coordinated Regional Downscaling Experiment (CORDEX), development of hierarchical model evaluation approaches, and successful research in the analysis and diagnosis of climate model skill, as well as the understanding and modeling of regional climate processes in North America. As part of the overarching goal, the project worked to disseminate a suite of methodologies, algorithms, and software components that the wider community can employ to advance climate science and applications. With rigorous demonstration, the evaluation framework and the mix of standard and high risk / high reward approaches helped form the basis for future development of a computationally enabled user-friendly system for community use.

17 WIND ENERGY↗
DOE OSTI2024

MSD CoP Webinar: Advancing MSD Research with Artificial Intelligence

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Recent advances in Artificial Intelligence (AI) are quickly changing the landscape of tools available to conceptualize, execute, and disseminate research. We posit that research efforts in Multi-Sector Dynamics can benefit from these advances; the new AI in MSD Working Group thus aims to identify and quantify opportunities and risks associated with their implementation. In this webinar, we will first introduce the new AI Working Group, which was initially conceived during the first MSD workshop in October 2023. Next, our panelists will explore how generative AI, explainable AI, and machine learning can help us improve modeling efforts in multiple domains, including climate science, hydrology, and energy systems. Finally, we will discuss the aims of the working group, gather inputs from the community, and suggest directions for the next steps. Presenters : Andrea Castelletti (Politecnico di Milano; Invited Speaker), Chaopeng Shen (Pennsylvania State University; Invited Speaker), Nicole Jackson (Sandia National Laboratory; Invited Speaker), Stefano Galelli (Cornell University; Co-Chair), David Gold (Utrecht University; Co-Chair), Jillian Sturtevant (Baylor University; Communications Officer) Moderator: Pat M. Reed (MSD CoP Facilitation Team) This webinar was held on: June 14th, 2024 from 12-1:30 PM EST

Artificial Intelligence↗
DOE OSTI2021

Description and Demonstration of the Coupled Community Earth System Model v2 – Community Ice Sheet Model v2 (CESM2‐CISM2)

Abstract Earth system/ice‐sheet coupling is an area of recent, major Earth System Model (ESM) development. This work occurs at the intersection of glaciology and climate science and is motivated by a need for robust projections of sea‐level rise. The Community Ice Sheet Model version 2 (CISM2) is the newest component model of the Community Earth System Model version 2 (CESM2). This study describes the coupling and novel capabilities of the model, including: (1) an advanced energy‐balance‐based surface mass balance calculation in the land component with downscaling via elevation classes; (2) a closed freshwater budget from ice sheet to the ocean from surface runoff, basal melting, and ice discharge; (3) dynamic land surface types; and (4) dynamic atmospheric topography. The Earth system/ice‐sheet coupling is demonstrated in a simulation with an evolving Greenland Ice Sheet (GrIS) under an idealized high CO 2 scenario. The model simulates a large expansion of ablation areas (where surface ablation exceeds snow accumulation) and a large increase in surface runoff. This results in an elevated freshwater flux to the ocean, as well as thinning of the ice sheet and area retreat. These GrIS changes result in reduced Greenland surface albedo, changes in the sign and magnitude of sensible and latent heat fluxes, and modified surface roughness and overall ice sheet topography. Representation of these couplings between climate and ice sheets is key for the simulation of ice and climate interactions.

Muntjewerf, Laura↗
DOE OSTI2025

Advancing the Representation of Human Actions in Large‐Scale Hydrological Models: Challenges and Future Research Directions

Characterizing the impact of human actions on terrestrial water fluxes and storages at multi-basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human-water interactions in large-scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (~10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper-resolution” hydrological models (~0.1–1 km), multi-sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human-water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper-resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort.

catchment hydrology↗
DOE OSTI2021

Initialized Earth system prediction from subseasonal to decadal timescales

Initialized Earth system predictions are made by starting a numerical prediction model in a state as consistent as possible to observations, and running it forward in time for up to ten years. Skillful predictions at time slices from subseasonal to seasonal (S2S), seasonal to interannual (S2I) and seasonal to decadal (S2D) offer information useful for various stakeholders, from agriculture to water resource management, and human and infrastructure safety. In this Review, we examine the processes influencing predictability, and discuss estimates of skill across S2S, S2I and S2D timescales. There are encouraging signs that skillful predictions can be made: at S2S timescales, there has been some skill in predicting the Madden-Julian Oscillation and North Atlantic Oscillation; at S2I in predicting the El Niño-Southern Oscillation; and at S2D, in predicting variability in North Atlantic sea surface temperatures. However, challenges remain, and future work must prioritise reducing model error, more effectively communicating forecasts to users, and increasing process and mechanistic understanding that could increase predictive skill and, in turn, confidence. As numerical models progress towards Earth system models, initialized predictions are expanding to include prediction of sea-ice, air pollution, terrestrial and ocean biochemistry which can bring clear benefit to society and various stakeholders.

climate prediction↗
DOE OSTI2025

Atmospheric and oceanic energy transport during North Atlantic freshening events: influences of moisture transport and hydrologic cycle feedbacks

Analogs of present-day rapid ice melt can be found in episodic discharges of icebergs that occurred during glacial periods called Heinrich events. This introduces excess meltwater into the North Atlantic and weakens the Atlantic thermohaline circulation (AMOC), triggering a hydrologic cycle–AMOC collapse feedback as the atmospheric energy transport compensates for reduced northward heat transport. Here we employ a novel series of 100-year North Atlantic “hosing” simulations to investigate atmospheric and oceanic energy transport response from freshwater forcing, focusing in particular on the role of atmospheric rivers (ARs) within atmospheric energy transport. Importantly, we use an “overwriting” methodology that allow us to attribute AMOC weakening to added North Atlantic meltwater and subsequent hydrologic cycle responses, respectively. In contrast to far-reaching response of transient eddies, our results show a substantial increase in moisture convergence from ARs that is geographically constrained to the North Atlantic midlatitudes. Such AR changes nevertheless comprise an important component of net precipitation changes over the Euro-Atlantic sector, with the amount being comparable to that from transient eddies over the subpolar Atlantic. Over the course of the century-long simulations, we demonstrate that hydrologic cycle responses to North Atlantic freshening and subsequent feedbacks, including those from ARs, account for approximately half of the simulated AMOC collapse. Furthermore, our work highlights the dynamics of atmospheric moisture transport response to North Atlantic freshening events and elucidates how intensifying moisture transport may accelerate AMOC collapse in the future.

Atmospheric Science↗
DOE OSTI2021

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

Abstract Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quantifying the drivers and predictability of seasonal changes in African fire

Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Abstract Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Factors controlling the oxygen isotopic composition of lacustrine authigenic carbonates in Western China: implications for paleoclimate reconstructions

Abstract In the carbonate-water system, at equilibrium, the oxygen isotopic composition of carbonate is dependent not only on the temperature but also on the isotopic composition of host water in which the carbonate is formed. In this study, lake surface sediment and water samples were collected from 33 terminal lakes in Western China to evaluate controls on the oxygen isotopic composition of lacustrine authigenic carbonates (δ 18 O carb ) and its spatial distribution. Our results show that water oxygen isotopic composition (δ 18 O water ) rather than lake summer water temperature (T water ), is the main determinant of δ 18 O carb , irrespective of whether oxygen isotope equilibrium is achieved. There are significant linear correlations between δ 18 O carb and elevation, as well as that between δ 18 O carb and latitude for lakes located on the Tibetan Plateau. In Western China, the spatial distribution of δ 18 O carb is consistent with that of δ 18 O water , and is ultimately controlled by the isotopic composition of local precipitation (δ 18 O precipitation ) that depends on the source of water vapor. Therefore, changes in δ 18 O carb can be predominantly interpreted as variations of δ 18 O water , which in turn represent changes in δ 18 O precipitation for paleoclimate reconstructions in this region, and may be relevant to studies of other areas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Earlier snowmelt may lead to late season declines in plant productivity and carbon sequestration in Arctic tundra ecosystems

Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO 2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO 2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO 2 later in the season.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

4D structural changes and pore network model of biomass during pyrolysis

Biochar is an engineered carbon-rich substance used for soil improvement, environmental management, and other diverse applications. To date, the understanding of how biomass affects biochar microstructure has been limited due to the complexity of analysis involved in tracing the changes in the physical structure of biomass as it undergoes thermochemical conversion. In this study, we used synchrotron x-ray micro-tomography to visualize changes in the internal structure of biochar from diverse feedstock (miscanthus straw pellets, wheat straw pellets, oilseed rape straw pellets, and rice husk) during pyrolysis by collecting a sequence of 3D scans at 50 °C intervals during progressive heating from 50 °C to 800 °C. The results show a strong dependence of biochar porosity on feedstock as well as pyrolysis temperature, with observed porosity in the range of 7.41–60.56%. Our results show that the porosity, total surface area, pore volume, and equivalent diameter of the largest pore increases with increasing pyrolysis temperature up to about 550 °C. The most dramatic development of pore structure occurred in the temperature range of 350–450 °C. This understanding is pivotal for optimizing biochar’s properties for specific applications in soil improvement, environmental management, and beyond. By elucidating the nuanced variations in biochar’s physical characteristics across different production temperatures and feedstocks, this research advances the practical application of biochar, offering significant benefits in agricultural, environmental, and engineering contexts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Neural network representations of multiphase Equations of State

Abstract Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Climate Science. The three desired properties of a general Equation of State model are adherence to the Laws of Thermodynamics, incorporation of phase transitions, and multiscale accuracy. Analytic models that adhere to all three are hard to develop and cumbersome to work with, often resulting in sacrificing one of these elements for the sake of efficiency. In this work, two deep-learning methods are proposed that provably satisfy the first and second conditions on a large-enough region of thermodynamic variable space. The first is based on learning the generating function (thermodynamic potential) while the second is based on structure-preserving, symplectic neural networks, respectively allowing modifications near or on phase transition regions. They can be used either “from scratch” to learn a full Equation of State, or in conjunction with a pre-existing consistent model, functioning as a modification that better adheres to experimental data. We formulate the theory and provide several computational examples to justify both approaches, highlighting their advantages and shortcomings.

Science & Technology - Other Topics↗
DOE OSTI2021

Hyperspectral interference tomography of nacre

Significance We invented an optical technique—hyperspectral interference tomography—that rapidly and nondestructively extracts nanoscale structural information across large samples of nacre (mother-of-pearl) and other layered materials by combining multiangle and polarization-resolved hyperspectral imaging with optical-interference modeling. We investigated nacre in mollusk shells from two different species, red abalone and rainbow abalone, and discovered a previously unknown relationship between the age of the mollusk and the thickness of aragonite tablets in nacre. Hyperspectral interference tomography will have applications in climate science, since nacre tablet thickness in fossil shells is a proxy for ancient seawater temperature, and in bioinspired mechanics, because the layered structure of nacre inspires engineered materials with exceptional strength and toughness.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Evaluating probabilistic deep learning methods for uncertainty quantification of temperature downscaling

Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpretability and reliability of predictions—key factors for climate risk assessment and decision-making. This study develops a DL model to downscale 2 m temperature across the contiguous United States using reanalysis datasets. We systematically evaluate three epistemic UQ methods—deep ensembles (DEns), Monte Carlo dropout (MCD), and Flipout—based on their probabilistic accuracy, downscaling performance, sensitivity to geographical features, and computational efficiency. Results indicate that MCD generally outperforms Flipout and DEns in terms of calibration and downscaling accuracy. However, DEns demonstrate lower calibration errors in coastal regions, indicating its higher confidence within these areas. Flipout, in contrast, is more sensitive to elevation gradients and exhibits higher calibration errors in mountainous regions. Hence, the choice of UQ method for this task depends on the specific requirements of the application. For applications that prioritize overall calibration, downscaling accuracy, and computational efficiency, MCD is a strong candidate. These findings highlight the importance of selecting UQ methods based on application-specific requirements, such as geographical context and computational constraints. By addressing the trade-offs between UQ methods, this study provides actionable insights for improving the reliability, scalability, and utility of DL-based downscaling in climate science.

Environmental sciences↗
DOE OSTI2024

Simulated Feasibility of 3-D Lightning Mapping From Space

In addition to the awe it inspires, lightning can illuminate the microphysical processes hidden away within deep convection. The current generation of space-based lightning mapping uses mostly 2-D optical imaging to connect overall flash characteristics to their parent storm dynamics, but are missing a dimension’s worth of information. With lightning now classified as an essential climate variable, future spaceborne mappers will need improved capabilities to take advantage of the 3-D structure of lightning flashes to support meteorological and climate modeling. We report here on a study of the feasibility of high-resolution 3-D lightning mapping using a radio frequency (RF)-based network of satellites from low-Earth orbit (LEO). Lightning sources are simulated using existing lightning mapping array (LMA) tools, modified for orbital detection, and spatially reconstructed using a Levenberg–Marquardt geolocation algorithm to assess sources of uncertainty in these solutions. We analyze the benefits and limitations of this approach compared to existing orbital and ground-based methods. Results of this study show that lightning can be mapped in 3-D with a vertical location accuracy better than 2 km using as few as five satellites in LEO capable of measuring the time-of-arrival of impulsive RF signals in the very high-frequency (VHF) band. The consequence of this study is that high-resolution, spaceborne 3-D mapping of lightning is achievable across most of the globe, having crucial implications for our understanding of not only lightning, but also severe weather development, climate science, and more.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Seasonal variations of the atmospheric muon neutrino spectrum measured with IceCube

This study presents an analysis of seasonal variations in the atmospheric muon neutrino flux, using 11.3 years of data from the IceCube Neutrino Observatory. By leveraging a novel spectral unfolding method, we explore the energy range from 125 GeV to 10 TeV for zenith angles from 90° to 110°, corresponding to the Antarctic atmosphere. Our findings reveal that the differential measurement of the amplitudes of the seasonal variation is consistent with an energy-dependent decrease reaching (-4.5 ± 1.2)% during Austral winter and increase to (+ 3.9 ± 1.3)% during Austral summer relative to the annual average at 10 TeV. While the unfolded flux exceeds the model predictions by up to 30%, the differential measurement of the seasonal to annual average flux remains unaffected. The measured seasonal variations of the muon neutrino spectrum are consistent with theoretical predictions using the MCEq code and the NRLMSISE-00 atmospheric model.

Astroparticle Physics and High-Energy Cosmic Pheno↗
DOE OSTI2022

C3F: Collaborative Container-based Model Coupling Framework

Solving complex real-world grand challenge problems requires in-depth collaboration of researchers from multiple disciplines. Such collaboration often involves harnessing multiscale and multi-dimensional data and combining models from different fields to simulate systems. However, the progress on this front has been limited mainly due to significant gaps in domain knowledge and tools that are typically employed in silos of the domains. Researchers from different fields face considerable barriers to understanding and reusing each other’s data/models in order to collaborate effectively. For example, in solving the global sustainability problems, researchers from hydrology, climate science, agriculture, and economics need to run their respective models to study different components of the global and local food, energy and water systems while, at the same time, need to interact with other researchers and integrate the results of one model with another. Developing this kind of model coupling workflow calls for (1) a large amount of data being processed and exchanged across domains and organizations, (2) identifying and processing the output of one model to make it ready for integration into another model, (3) controlling the workflow dynamically so that it runs until a certain convergence condition or other criteria is met, and (4) close collaboration among the modelers to explore, tune, and test the configuration and data transformation needed to link the models. We have developed C3F, a flexible collaborative model coupling framework to help researchers accelerate their model integration and linking efforts by leveraging advanced cyberinfrastructure such as high-performance computing and virtual containers. In this paper, we describe our experience and lessons learned in developing this cyberinfrastructure solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded INFEWS project and a DOE-funded Program on Coupled Human and Earth Systems (PCHES) to study the implications of groundwater scarcity for food-energy-water systems. The C3F model coupling framework can be extended to facilitate other model linkages as well.

containerization↗