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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Nonlinear Response of Extreme Precipitation to Warming in CESM1

The response of extreme precipitation to warming varies widely among climate models, especially in the tropics. In some models, there have been indications that the rate of response increases with warming – that the response is not linear. In this work, we investigate the evolution of extreme precipitation, quantified by the maximum accumulated precipitation in a day each year, in CESM1. We find that tropical- and global-average extreme precipitation are related to global mean surface temperature quadratically. This behavior is associated with an increase in the large scale fraction of extreme precipitation, and also strengthening circulation on extreme 25 precipitation days. Compared to other CMIP5 models, the nonlinearity in CESM1 is among the largest. One implication is that the difference between CESM1 simulations with full forcing and with fixed aerosols cannot be used to isolate the response of extreme precipitation to aerosols, as the resulting climates are not equally warm.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Representing Socio‐Economic Uncertainty in Human System Models

Abstract Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate-related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Role of morphology in defect formation and photo-induced carrier instabilities in amorphous indium oxide

Ab initio molecular dynamics liquid-quench simulations and hybrid density functional calculations are performed to model the effects of room-temperature atomic fluctuations and photo-illumination on the structural and electronic properties of amorphous sub-stoichiometric In 2 O 2.96 . A large configurational ensemble is employed to reliably predict the distribution of localized defects as well as their response to the thermal and light activation. The results reveal that the illumination effects on the carrier concentration are greater in amorphous configurations with shorter In–O bond length and reduced polyhedral sharing as compared to the structures with a more uniform morphology. Further, the obtained correlation between the photo-induced carrier density and the reduction in the number of fully coordinated In-atoms implies that metal oxides with a significant fraction of crystalline/amorphous interfaces would show a more pronounced response to illumination. Photo-excitation also produces In–O 2 –In defects that have not been previously found in sub-stoichiometric amorphous oxides; these defects are responsible for carrier instabilities due to overdoping.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Anthropogenic influence on the drivers of the Western Cape drought 2015–2017

In the period 2015–2017, the Western Cape region has suffered from three consecutive years of below average rainfall—leading to a prolonged drought and acute water shortages, most prominently in the city of Cape Town. After testing that the precipitation deficit is the primary driver behind the reduced surface water availability, we undertake a multi-method attribution analysis for the meteorological drought, defined in terms of a deficit in the 3 years running mean precipitation averaged over the Western Cape area. The exact estimate of the return time of the event is sensitive to the number of stations whose data is incorporated in the analysis but the rarity of the event is unquestionable, with a return time of more than a hundred years. Synthesising the results from five different large model ensembles as well as observed data gives a significant increase by a factor of three (95% confidence interval 1.5–6) of such a drought to occur because of anthropogenic climate change. All the model results further suggest that this trend will continue with future global warming. These results are in line with physical understanding of the effect of climate change at these latitudes and highlights that measures to improve Cape Town's resilience to future droughts are an adaptation priority.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantifying airborne fraction trends and the destination of anthropogenic CO 2 by tracking carbon flows in a simple climate model

Abstract Atmospheric carbon dioxide (CO 2 ) concentrations have increased as a direct result of human activity and are at their highest level over the last 2 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO 2 , the fate of anthropogenic CO 2 over time, and the likelihood of a trend in the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model with the novel ability to explicitly track carbon as it flows through the Earth system. We use key model parameters in a Monte Carlo analysis of 15 000 model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much anthropogenic carbon ends up in the atmosphere. Modeled airborne fraction was roughly 52%, consistent with observational studies. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960–2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. However, the percentage of atmospheric CO 2 derived from anthropogenic origins can be much higher because of Earth system feedbacks. We find it peaks at over 90% between 2010–2050. Moreover, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics of anthropogenic CO 2 in the Earth system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2011

Perspectives for laboratory implementation of the Duan-Lukin-Cirac-Zoller protocol for quantum repeaters

We analyze the efficiency and scalability of the Duan-Lukin-Cirac-Zoller (DLCZ) protocol for quantum repeaters focusing on the behavior of the experimentally accessible measures of entanglement for the system, taking into account crucial imperfections of the stored entangled states. We calculate then the degradation of the final state of the quantum-repeater linear chain for increasing sizes of the chain, and characterize it by a lower bound on its concurrence and the ability to violate the Clausner-Horne-Shimony-Holt inequality. The states are calculated up to an arbitrary number of stored excitations, as this number is not fundamentally bound for experiments involving large atomic ensembles. The measurement by avalanche photodetectors is modeled by ''ON/OFF'' positive operator-valued measure operators. As a result, we are able to consistently test the approximation of the real fields by fields with a finite number of excitations, determining the minimum number of excitations required to achieve a desired precision in the prediction of the various measured quantities. This analysis finally determines the minimum purity of the initial state that is required to succeed in the protocol as the size of the chain increases. We also provide a more accurate estimate for the average time required to succeed in each step of the protocol. The minimum purity analysis and the new time estimates are then combined to trace the perspectives for implementation of the DLCZ protocol in present-day laboratory setups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Near-field probing of strong light-matter coupling in single IR antennae

Quantum well intersubband polaritons are traditionally studied in large scale ensembles, over many wavelengths in size.In this presentation, we demonstrate that it is possible to detect and investigate intersubband polaritons in a single sub-wavelength nanoantenna in the IR frequency range. We observe polariton formation using a scattering-type near-fieldmicroscope and nano-FTIR spectroscopy. In this work, we will discuss near-field spectroscopic signatures of plasmonic antennae withand without coupling to the intersubband transition in quantum wells located underneath the antenna. Evanescent fieldamplitude spectra recorded on the antenna surface show a mode anti-crossing behavior in the strong coupling case. Wealso observe a corresponding strong-coupling signature in the phase of the detected field. We anticipate that this near-fieldapproach will enable explorations of strong and ultrastrong light-matter coupling in the single nanoantenna regime,including investigations of the elusive effect of ISB polariton condensation.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Coherent cooling of atoms in a frequency-modulated standing laser wave: Wave function and stochastic trajectory approaches

The wave function of a moderately cold atom in a stationary near-resonant standing light wave delocalizes very fast due to wave packet splitting. However, we show that frequency modulation of the field can suppress packet splitting for some atoms whose specific velocities are in a narrow range. These atoms remain localized in a small space for a long time. We demonstrate and explain this effect numerically and analytically. We also demonstrate that the modulated field can not only trap but also cool the atoms. We perform a numerical experiment with a large atomic ensemble having wide initial velocity and energy distributions. During the experiment, most of atoms leave the wave while the trapped atoms have a narrow energy distribution.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Unified many-worlds browsing of arbitrary physics-based animations

Manually tuning physics-based animation parameters to explore a simulation outcome space or achieve desired motion outcomes can be notoriously tedious. This problem has motivated many sophisticated and specialized optimization-based methods for fine-grained (keyframe) control, each of which are typically limited to specific animation phenomena, usually complicated, and, unfortunately, not widely used. In this paper, we propose Unified Many-Worlds Browsing (UMWB), a practical method for sample-level control and exploration of physics-based animations. Our approach supports browsing of large simulation ensembles of arbitrary animation phenomena by using a unified volumetric WORLDPACK representation based on spatiotemporally compressed voxel data associated with geometric occupancy and other low-fidelity animation state. Beyond memory reduction, the WORLDPACK representation also enables unified query support for interactive browsing: it provides fast evaluation of approximate spatiotemporal queries, such as occupancy tests that find ensemble samples ("worlds") where material is either IN or NOT IN a user-specified spacetime region. WORLDPACKS also support real-time hardware-accelerated voxel rendering by exploiting the spatially hierarchical and temporal RLE raster data structure. Our UMWB implementation supports interactive browsing (and offline refinement) of ensembles containing thousands of simulation samples, and fast spatiotemporal queries and ranking. We show UMWB results using a wide variety of physics-based animation phenomena---not just JELL-O ® .

Computer Science↗
DOE OSTI2024

Exascale Dislocation Simulator

ExaDiS, or Exascale Dislocation Simulator, is a set of software modules written to enable numerical simulations of large groups of moving and interacting dislocations, line defects in crystals responsible for crystal plasticity. By tracking time evolution of sufficiently large dislocation ensembles, ExaDiS predicts plasticity response and plastic strength of crystalline materials. ExaDiS is built around a library of core functions for Discrete Dislocation Dynamics method specifically written to perform efficient computations on GPUs.

Bertin, Nicolas↗
DOE OSTI2020

Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

Studies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Area 2: Inexpensive Monitoring and Uncertainty Assessment of CO 2 Plume Migration using Injection Data

In-depth understanding of the long-term fate of CO₂ in the subsurface requires study and analysis of the reservoir formation, the overlaying caprock formation, and adjacent faults. Because there is significant uncertainty in predicting the location and extent of geologic heterogeneity that can impact the future migration of CO₂ in the subsurface, there is a need to develop algorithms that can reliably quantify this uncertainty in plume migration. This project is focused on the development of a model selection algorithm that refines an initial suite of subsurface models representing the prior uncertainty to create a posterior set of subsurface models that reflect injection performance consistent with that observed. Such posterior models can be used to represent uncertainty in the future migration of the CO₂ plume. Because only injection data is required, the method provides a very inexpensive method to map the migration of the plume and the associated uncertainty in migration paths. The model selection method developed as part of this project mainly consists of assessing the connectivity/dynamic characteristics of a large prior ensemble of models, grouping the models on the basis of their expected dynamic response, selecting the subgroup of models that most closely yield dynamic response closest to the observed dynamic data, and finally quantifying the uncertainty in plume migration using the selected subset of models. The main accomplishment of the project is the development of a software module within the SGEMS earth modeling software package that implements the model selection methodology. This software module was subsequently applied to analyze CO₂ plume migration in two field projects – the In Salah CO₂ Injection project in Algeria and CO₂ injection into the Utsira formation in Norway. These applications of the software revealed that the proxies developed in this project for quickly assessing the dynamic characteristics of the reservoir were highly efficient and yielded accurate grouping of reservoir models. The plume migration paths probabilistically assessed by the method were confirmed by field observations and auxiliary data. The report also documents the application of the software to answer practical questions such as the optimum location of monitoring wells to reliably assess the migration of CO₂ plume, the effect of CO₂-rock interactions on plume migration and the ability to detect the plume under those conditions and the effect of a slow, unresolved leak on the predictions of plume migration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Model Selection for Monitoring CO2 Plume during Sequestration

The model selection method developed as part of this project mainly includes four steps: (1) assessing the connectivity/dynamic characteristics of a large prior ensemble of models, (2) model clustering using multidimensional scaling coupled with k-mean clustering, (3) model selection using the Bayes' rule in the reduced model space, (4) model expansion using iterative resampling of the posterior models. The fourth step expresses one of the advantages of the method: it provides a built-in means of quantifying the uncertainty in predictions made with the selected models. In our application to plume monitoring, by expanding the posterior space of models, the final ensemble of representations of geological model can be used to assess the uncertainty in predicting the future displacement of the CO2 plume. The software implementation of this approach is attached here.

Source record↗
DOE OSTI2019

Quantifying stochastic uncertainty in detection time of human-caused climate signals

Significance Climate observations comprise one sequence of natural internal variability and the response to external forcings. Large initial condition ensembles (LEs) performed with a single climate model provide many different sequences of internal variability and forced response. LEs allow analysts to quantify random uncertainty in the time required to detect forced “fingerprint” patterns. For tropospheric temperature, the consistency between fingerprint detection times in satellite data and in 2 different LEs depends primarily on the size of the simulated warming in response to greenhouse gas increases and the simulated cooling caused by anthropogenic aerosols. Consistency is closest for a model with high sensitivity and large aerosol-driven cooling. Assessing whether this result is physically reasonable will require reducing currently large aerosol forcing uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

ENSO's Changing Influence on Temperature, Precipitation, and Wildfire in a Warming Climate

On interannual to decadal time scales, the climate mode with many of the strongest societal impacts is the El Niño–Southern Oscillation (ENSO). However, quantifying ENSO's changes in a warming climate remains a formidable challenge, due to both the noise arising from internal variability and the complexity of air-sea feedbacks in the tropical Pacific Ocean. In this paper, we use large (≥30-member) ensembles of climate simulations to show that anthropogenic climate change can produce systematic increases in ENSO teleconnection strength over many land regions, driving increased interannual variability in regional temperature extremes and wildfire frequency. As the spatial character of this intensification exhibits strong land-ocean contrasts, a causal role for land-atmosphere feedbacks is suggested. The identified increase in variance occurs in multiple model ensembles, independent of changes in sea surface temperature variance. In conclusion, this suggests that in addition to changes in the overall likelihoods of heat and wildfire extremes, the variability in these events may also be a robust feature of future climate.

54 ENVIRONMENTAL SCIENCES↗