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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hybrid Particle Method for Computational Shock Physics

A long-standing area of research for Eulerian shock wave physics codes has been the treatment of strength and damage for materials. Here we present a method that will aid in the analysis of strength and failure in shock physics applications where excessive diffusion of critical variables can occur and control the solution outcome. Eulerian methods excel for large deformation simulations in general but are inaccurate in capturing structural behavior. Lagrangian methods provide better structural response, but finite element meshes can become tangled. Therefore, a technique for merging Lagrangian and Eulerian treatments of material response, within a single numerical framework, was implemented in the Multiple Component computational shock physics hydrocode. The capability is a Lagrangian/Eulerian Particle Method (LEPM) that uses particles to interface a Lagrangian treatment of material strength with a more traditional Eulerian treatment of the Equation of State (EOS). Lagrangian numerical methods avoid the advection diffusion found in Eulerian methods, which typically strongly affects strength constitutive law internal variables, such as equivalent plastic strain, porosity and/or damage. The Lagrangian capability enhances existing capabilities and permits accurate predictions of high rate, large deformation and/or shock of mechanical structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Mechanisms of tropical Pacific decadal variability

Naturally occurring tropical Pacific variations at timescales of 7–70 years — tropical Pacific decadal variability (TPDV) — describe basin-scale sea surface temperature (SST), sea-level pressure and heat content anomalies. Several mechanisms are proposed to explain TPDV, which can originate through oceanic processes, atmospheric processes or as an El Niño/Southern Oscillation (ENSO) residual. In this Review, we synthesize knowledge of these mechanisms, their characteristics and contribution to TPDV. Oceanic processes include off-equatorial Rossby waves, which mediate oceanic adjustment and contribute to variations in equatorial thermocline depth and SST; variations in the strength of the shallow upper-ocean overturning circulation, which exhibit a large anti-correlation with equatorial Pacific SST at interannual and decadal timescales; and the propagation of salinity-compensated temperature (spiciness) anomalies from the subtropics to the equatorial thermocline. Atmospheric processes include midlatitude internal variability leading to tropical and subtropical wind anomalies, which result in equatorial SST anomalies and feedbacks that enhance persistence; and atmospheric teleconnections from Atlantic and Indian Ocean SST variability, which induce winds conducive to decadal anomalies of the opposite sign in the Pacific. Although uncertain, the tropical adjustment through Rossby wave activity is likely a dominant mechanism. Finally, a deeper understanding of the origin and spectral characteristics of TPDV-related winds is a key priority.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1999

An Assessment of the Impact of the 1997-98 El Nino on the Asian-Australian Monsoon

Using state-of-the-art satellite-gauge monthly rainfall estimate and optimally interpolated sea surface temperature (SST) data, we have assessed the 1997-98 AA-monsoon anomalies in terms of three basic causal factors: basin-scale SST, regional coupling, and internal variability. Singular Value Decomposition analyses of rainfall and SST are carried out globally over the entire tropics and regionally over the AA-monsoon domain. Contributions to monsoon rainfall predictability by various factors are evaluated from cumulative anomaly correlation with dominant regional SVD modes. Results reveal a dominant, large-scale monsoon-El Nino coupled mode with well-defined centers of action in the near-equatorial monsoon regions during the boreal summer and winter respectively. The observed 1997-98 AA-monsoon anomalies are found to be very complex with approximately 34% of the anomalies of the Asian (boreal) summer monsoon and 74% of the Australia (austral) monsoon attributable to basin-scale SST influence associated with El Nino. Regional coupled processes contribute an additional 19% and 10%, leaving about 47% and 16% due to internal dynamics for the boreal and austral monsoon respectively. For the boreal summer monsoon, it is noted that the highest monsoon predictability is not necessary associated with major El Nino events (e.g. 1997, 1982) but rather in non-El Nino years (e.g. 1980, 1988) when contributions from the regional coupled modes far exceed those from the basin-scale SST. The results suggest that in order to improve monsoon seasonal-to-interannual predictability, there is a need to exploit not only monsoon-El Nino relationship, but also intrinsic monsoon regional coupled processes.

Lau, K.-M.↗
NASA NTRS1999

Assessment of the 1997-1998 Asian Monsoon Anomalies

Using State-of-the-art satellite-gauge monthly rainfall estimate and optimally interpolated sea surface temperature (SST) data, we have assessed the 1997-98 Asian monsoon anomalies in terms of three basic causal factors: basin-scale SST, regional coupling, and internal variability. Singular Value Decomposition analysis of rainfall and SST are carried out globally over the entire tropics and regionally over the Asian monsoon domain. Contributions to monsoon rainfall predictability by various factors are evaluated from cumulative anomaly correlation with dominant regional SVD modes. Results reveal a dominant, large-scale monsoon-El Nino coupled mode with well-defined centers of action in the near-equatorial monsoon regions. it is noted that some subcontinental regions such as all-India, or arbitrarily chosen land regions over East Asia, while important socio-economically, are not near the centers of influence from El Nino, hence are not necessarily representative of the response of the entire monsoon region to El Nino. The observed 1997-98 Asian monsoon anomalies are found to be very complex with approximately 34% of the anomalies attributable to basin- scale SST influence associated with El Nino. Regional coupled processes contribute an additional 19%, leaving about 47% due to internal dynamics. Also noted is that the highest monsoon predictability is not necessary associated with major El Nino events (e.g. 1997, 1982) but rather in non-El Nino years (e.g. 1980, 1988) when contributions from the regional coupled modes far exceed those from the basin-scale SST. The results suggest that in order to improve monsoon seasonal-to-interannual predictability, there is a need to exploit not only monsoon-El Nino relationship, but also monsoon regional coupled processes and their modulation by long-term climate change.

Lau, William K.-M.↗
DOE OSTI2026

A theoretical index for understanding distinct land relative humidity trends in observations, reanalyses, and models

Land surface relative humidity (RH) is a key variable in the coupled land–atmosphere system that profoundly influences terrestrial hydroclimate and ecosystems. Yet historical changes in land RH are not well understood due to limited observations, biased reanalyses, and the lack of a framework for interpreting RH changes under multiple influencing factors. Here, we show that the spatiotemporal variability of land RH and its distinct historical trends among observations, reanalyses, and Earth system models are captured by a simple index based on the ratio of precipitation (P) to a modified potential evapotranspiration formulated independently of RH (PET°). The index provides a physical calibration of biased land RH in reanalyses and a quantitative framework for interpreting land RH changes. Over 1973–2024, land RH has decreased substantially, owing to the intrinsic rise in PET° with temperature and little increase in land precipitation. Reanalyses overestimate the observed RH decrease, consistent with exaggerated surface warming and precipitation decline. The index captures this coherent bias and enables a calibration using observed precipitation and temperature. Models simulate a wide range of land RH trends, but nearly all runs underrepresent the historical drying. The index captures the model spread and discrepancy and attributes them to contributions of precipitation and PET°. Weaker land RH decreases in models arise mainly from weaker subtropical precipitation declines, linked to muted intensification of subtropical highs and biased subtropical climatology. The model–observation discrepancy is unlikely explained by internal variability, implying model underestimation of forced RH decrease and a drier land future than current projections.

Earth system models↗
NASA NTRS2019

Climate Change Amplification of Natural Drought Variability: The Historic Mid-Twentieth Century North American Drought in a Warmer World

In the mid-twentieth century (1948-1957), North America experienced a severe drought forced by cold tropical Pacific sea surface temperatures (SSTs). If these SSTs recurred, it would likely cause another drought, but in a world substantially warmer than the one in which the original event took place. We use a 20-member ensemble of the GISS climate model to investigate the drought impacts of a repetition of the mid-twentieth century SST anomalies in a significantly warmer world. Using observed SSTs and mid-twentieth century forcings (Hist-DRGHT), the ensemble reproduces the observed precipitation deficits during the cold season (October–March) across the Southwest, Southern Plains, and Mexico and during the warm season (April–September) in the Southern Plains and the Southeast. Under analogous SST forcing and enhanced warming (Fut-DRGHT, ≈3 K above pre-industrial), cold season precipitation deficits are ameliorated in the Southwest and Southern Plains and intensified in the Southeast, while during the warm season precipitation deficits are enhanced across North America. This occurs primarily from greenhouse gas forced trends in mean precipitation, rather than changes in SST teleconnections. Cold season runoff deficits in Fut-DRGHT are significantly amplified over the Southeast, but otherwise similar to Hist-DRGHT over the Southwest and Southern Plains. In the warm season, however, runoff and soil moisture deficits during Fut-DRGHT are significantly amplified across the southern US, a consequence of enhanced precipitation deficits and increased evaporative losses due to warming. Our study highlights how internal variability and greenhouse gas forced trends in hydroclimate are likely to interact over North America, including how changes in both precipitation and evaporative demand will affect future drought.

Benjamin I Cook↗
DOE OSTI2025

Towards provision of regularly updated climate data from the Coupled Model Intercomparison Project

The Coupled Model Intercomparison Project (CMIP) is a flagship of the World Climate Research Programme (WCRP). CMIP has become a recognised ‘brand’ in climate circles evolving over the last thirty years from a targeted research activity by a small number of climate modelling centres intercomparing their Earth System Model (ESM) simulations to a broad international coordinated research effort (Durack et al, 2025). CMIP is organized as a research activity leveraging funded and in-kind contributions from experts within modelling centres and the broader scientific community supported more recently by a fully-funded International Project Office. Within CMIP, Model Intercomparison Projects (MIPs) are community-designed to understand past, present and future climate. CMIP data provides a valuable resource for climate research and is routinely used to assess model representation of climate processes and test scientific hypotheses in the context of model uncertainty and (forced and internal) variability as evident from its prolific use in scientific publications1 . The impact relies on enabling infrastructure (most prominently via the Earth System Grid Federation (ESGF)), which allows sharing of simulation output, provision of the boundary conditions used in each simulation, and definition of the data standards that are essential to facilitating wide use of the data. The impact is supplemented by the wide-ranging scrutiny to which model simulations are subjected. Beyond its use in research, CMIP data is a key resource for communities producing derived climate information from downscaling and impact studies, such as the Coordinated Regional Downscaling Experiment (CORDEX; Gutowski et al., 2016) and the Intersectoral Impacts MIP (ISIMIP; Frieler et al., 2024). Government, academic and commercial entities also increasingly rely on CMIP and its downstream data for climate risk assessments and climate services (for example, Copernicus Climate Change Service and World Bank portal). This means that, although CMIP is a research activity, it increasingly serves a secondary and very relevant role as a provider of climate data – a long-recognised dichotomy (Stevens, 2024). Research and applications have distinct needs, with the former requiring flexibility and generality and the latter consistency. Here we explain how the design of the research activity has been adapted to reduce the burdens imposed by applications and how the research infrastructure might evolve to further enable scientific inquiry. We propose one possible approach to consistently providing model information and projections for applications in the future.

Environmental sciences↗
DOE OSTI2021

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

Abstract As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A framework for detection and attribution of regional precipitation change: Application to the United States historical record

Abstract Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spatio-temporal multivariate cluster evolution analysis for detecting and tracking climate impacts

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. Here, this paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

Anomaly detection↗
DOE OSTI2022

A unified finite strain gradient-enhanced micropolar continuum approach for modeling quasi-brittle failure of cohesive-frictional materials

Here, in this work, a novel framework for modeling quasi-brittle crack propagation and shear band dominated failure of cohesive-frictional materials like concrete, mortar, rock, tough ceramics, energetic materials, but also granular materials like sands or powders in terms of a unified continuum approach is proposed. It is based on a combination of the gradient-enhanced continuum with gradients of internal variables for representing quasi-brittle cracking, and the micropolar continuum, accounting for the deformation of the microstructure. For developing the gradient-enhanced micropolar framework, the set of balance equations and the kinematic relations are derived, and the constitutive relations are established in a general manner. The framework is formulated in a geometrically exact setting, based on the thermodynamically sound theory of hyperelasto-plasticity, and the numerical implementation by means of the finite element method is discussed. For assessing the approach, realizations of this new approach in terms of constitutive models for particular materials are developed. They are applied to numerical benchmark examples, investigating various loading conditions, and the obtained results are validated by means of a comparison with experiments from the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fully implicit crystal plasticity models representing orientations with modified Rodrigues parameters

Here, this work describes a crystal plasticity formulation combining several mathematical, numerical, and implementation choices to produce a highly efficient model. Specifically, the key choices in the implementation are (1) representing orientations with modified Rodrigues parameters, (2) implementing a fully coupled implicit time integration for the elastic stretch, the crystal orientations, and the model internal variables, (3) implementing the model in the NEML2 constitutive modeling framework, based on PyTorch, to vectorize the calculations and port the computation to GPUs and other hardware accelerators, and (4) an exact implementation of the consistent tangent matrix, even for arbitrary coupling to other field variables beyond the displacements, like temperature, neutron fluence, etc. The first two features of the model are, to our knowledge, novel. The paper considers each of these choices individually as well as the final model as a whole. This includes a full description of modified Rodrigues parameters, their advantages over other representations of orientations, the mathematical formulae and tools required to implement a model with modified Rodrigues parameters, and a detailed description of the geometry of the space of modified Rodrigues parameters (in an appendix). It also includes a description of a fully implicit time integration scheme for the orientations and the advantages in representing orientations with modified Rodrigues parameters in implementing such a model. The work then assess, via numerical examples, the advantages of fully coupled implicit time integration versus more common decoupled and explicit time integration schemes. These studies demonstrate the computational advantages of fully coupled integration versus other time integration algorithms, though the performance of the competing models depends on the complexity of the underlying single crystal model. The study concludes by demonstrating that the choice of time integration method affects the sharpness of the predicted texture, with explicit methods for integrating the orientations overestimating texture sharpness and implicit methods underestimating texture sharpness.

Crystal plasticity↗
DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

The Lack of QBO–MJO Connection in CMIP6 Models

Observational analysis has indicated a strong connection between the stratospheric quasi–biennial oscillation (QBO) and tropospheric Madden–Julian oscillation (MJO), with MJO activity being stronger during the easterly phase than the westerly phase of the QBO. We assess the representation of this QBO–MJO connection in 30 models participating in the Coupled Model Intercomparison Project 6. While some models reasonably simulate the QBO during boreal winter, none of them capture a difference in MJO activity between easterly and westerly QBO that is larger than that which would be expected from the random sampling of internal variability. The weak signal of the simulated QBO–MJO connection may be due to the weaker amplitude of the QBO than observed, especially between 100 to 50 hPa. This weaker amplitude in the models is seen both in the QBO–related zonal wind and temperature, the latter of which is thought to be critical for destabilizing tropical convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantifying the Occurrence of Record Hot Years Through Normalized Warming Trends

Surface air temperature trends and extreme events are of global concern and they are related. Here we show that the occurrence of record hot years over different latitudes from 1960-2019 are more strongly correlated with the observational annual mean temperature trends normalized by internal variability. Compared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over land. Two hot spots with more frequent occurrence of record hot years are identified: northern hemisphere ocean (versus land) and southern hemisphere tropical land (versus mid- and high-latitude lands). Ensemble mean results from 32 Earth system models agree with observations better than individual models, but they do not reproduce observed large differences in correlations across latitudes between normalized trends and record-breaking events over land versus ocean. Furthermore, our results enable the quantification of record hot year occurrence through normalized warming trends and provide new metrics for model evaluation and improvement.

58 GEOSCIENCES↗
DOE OSTI2021

Coupled Climate Responses to Recent Australian Wildfire and COVID-19 Emissions Anomalies Estimated in CESM2

In this work, multiple 50-member ensemble simulations with the Community Earth System Model version 2 are performed to estimate the coupled climate responses to the 2019–2020 Australian wildfires and COVID-19 pandemic policies. The climate response to the pandemic is found to be weak generally, with global-mean net top-of-atmosphere radiative anomalies of +0.23 ± 0.14 W m –2 driving a gradual global warming of 0.05 ± 0.04 K by the end of 2022. While regional anomalies are detectable in aerosol burdens and clear-sky radiation, few significant anomalies exist in other fields due to internal variability. In contrast, the simulated response to Australian wildfires is a strong and rapid cooling, peaking globally at –0.95 ± 0.15 W m –2 in late 2019 with a global cooling of 0.06 ± 0.04 K by mid-2020. Transport of fire aerosols throughout the Southern Hemisphere increases albedo and drives a strong interhemispheric radiative contrast, with simulated responses that are consistent generally with those to a Southern Hemisphere volcanic eruption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

The surface of the Great Lakes interacts with the atmosphere, influencing the weather and climate over the Great Lakes Region (GLR). However, most climate models were not designed with sufficient emphasis on lake–atmosphere interactions, which could potentially cause model biases over the GLR. To understand how lake surface temperature (LST) affects the regional summer climate over the GLR, we conducted twin experiments using the Weather Research and Forecasting model at a spatial resolution of 4 km using two different LST data sets as the bottom boundary condition over the Great Lakes. In this work, our simulations include 10 ensemble members for the summer of 2018 and a single multiyear run for the summers of 2014–2020. Results show that variations in LST influence atmospheric temperature and moisture at a local scale—while affecting the convective environment and precipitation processes over a much larger spatial scale. In particular, an LST that is only 1°C–3°C warmer (depending on the lake) increases near-surface air temperature by 1.93°C and 0.97°C over Lake Superior and Lake Erie, respectively, and increases evaporation over the lakes by 0.23 and 1.1 mm day –1 . The warmer LST reduces mesoscale convective precipitation upstream of the GLR; however, it increases isolated deep convective precipitation and nonconvective precipitation downstream of GLR due to increased local instability and enhancement of moisture transport. Our analyses confirm the robustness of these impacts, which is at least 2 times larger than the model internal variability and is seen across all simulated summer seasons.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Exploiting SMILEs and the CMIP5 Archive to Understand Arctic Climate Change Seasonality and Uncertainty

Arctic Amplification (AA) exhibits a distinct seasonal dependence; it is weakest in boreal summer and strongest in winter. Here, we analyze simulations from single-model initial-condition large ensembles and Coupled Model Intercomparison Project Phase 5 to decipher the seasonal evolution of Arctic climate change. Models agree that the annual maximum AA shifts from autumn into winter over the 21st century, accompanied by similar shifts in sea-ice loss and surface turbulent heat fluxes, whereas the maximum precipitation shifts only into late autumn. However, the exact seasonal timing and magnitude of these shifts are highly uncertain. Decomposing the uncertainty into model structural differences, emission scenarios, and internal variability reveals that model differences dominate the total uncertainty, which also undergo autumn-to-winter shifts. We also find that the scenario uncertainty is unimportant for projections of AA. These results highlight that understanding model differences is critical to reducing uncertainty in projected Arctic climate change.

54 ENVIRONMENTAL SCIENCES↗