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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Glutathionylation of Yersinia pestis LcrV and Its Effects on Plague Pathogenesis

Glutathionylation, the formation of reversible mixed disulfides between glutathione and protein cysteine residues, is a posttranslational modification previously observed for intracellular proteins of bacteria. Here we show that Yersinia pestis LcrV, a secreted protein capping the type III secretion machine, is glutathionylated at Cys 273 and that this modification promotes association with host ribosomal protein S3 (RPS3), moderates Y. pestis type III effector transport and killing of macrophages, and enhances bubonic plague pathogenesis in mice and rats. Secreted LcrV was purified and analyzed by mass spectrometry to reveal glutathionylation, a modification that is abolished by the codon substitution Cys 273 Ala in lcrV . Moreover, the lcrV C273A mutation enhanced the survival of animals in models of bubonic plague. Investigating the molecular mechanism responsible for these virulence attributes, we identified macrophage RPS3 as a ligand of LcrV, an association that is perturbed by the Cys 273 Ala substitution. Furthermore, macrophages infected by the lcrV C273A variant displayed accelerated apoptotic death and diminished proinflammatory cytokine release. Deletion of gshB , which encodes glutathione synthetase of Y. pestis , resulted in undetectable levels of intracellular glutathione, and we used a Y. pestis Δ gshB mutant to characterize the biochemical pathway of LcrV glutathionylation, establishing that LcrV is modified after its transport to the type III needle via disulfide bond formation with extracellular oxidized glutathione. IMPORTANCE Yersinia pestis , the causative agent of plague, has killed large segments of the human population; however, the molecular bases for the extraordinary virulence attributes of this pathogen are not well understood. We show here that LcrV, the cap protein of bacterial type III secretion needles, is modified by host glutathione and that this modification contributes to the high virulence of Y. pestis in mouse and rat models for bubonic plague. These data suggest that Y. pestis exploits glutathione in host tissues to activate a virulence strategy, thereby accelerating plague pathogenesis.

Microbiology↗
DOE OSTI2018

Large Blooms of Bacillales ( Firmicutes ) Underlie the Response to Wetting of Cyanobacterial Biocrusts at Various Stages of Maturity

Biological soil crusts (biocrusts) account for a substantial portion of primary production in dryland ecosystems. They successionally mature to deliver a suite of ecosystem services, such as carbon sequestration, water retention and nutrient cycling, and climate regulation. Biocrust assemblages are extremely well adapted to survive desiccation and to rapidly take advantage of the periodic precipitation events typical of arid ecosystems. Here we focus on the wetting response of incipient cyanobacterial crusts as they mature from “light” to “dark.” We sampled a cyanobacterial biocrust chronosequence before (dry) and temporally following a controlled wetting event and used high-throughput 16S rRNA and rRNA gene sequencing to monitor the dynamics of microbial response. Overall, shorter-term changes in phylogenetic beta diversity attributable to periodic wetting were as large as those attributable to biocrust successional stage. Notably, more mature crusts showed significantly higher resistance to precipitation disturbance. A large bloom of a few taxa within the Firmicutes , primarily in the order Bacillales , emerged 18 h after wetting, while filamentous crust-forming cyanobacteria showed variable responses to wet-up across the successional gradient, with populations collapsing in less-developed light crusts but increasing in later-successional-stage dark crusts. Overall, the consistent Bacillales bloom accompanied by the variable collapse of pioneer cyanobacteria of the Oscillatoriales order across the successional gradient suggests that the strong response of few organisms to a hydration pulse with the mortality of the autotroph might have important implications for carbon (C) balance in semiarid ecosystems. IMPORTANCE Desert biological soil crusts are terrestrial topsoil microbial communities common to arid regions that comprise 40% of Earth’s terrestrial surface. They successionally develop over years to decades to deliver a suite of ecosystem services of local and global significance. Ecosystem succession toward maturity has been associated with both resistance and resilience to disturbance. Recent work has shown that the impacts of both climate change and physical disturbance on biocrusts increase the potential for successional resetting. A larger proportion of biocrusts are expected to be at an early developmental stage, hence increasing susceptibility to changes in precipitation frequencies. Therefore, it is essential to characterize how biocrusts respond to wetting across early developmental stages. In this study, we document the wetting response of microbial communities from a biocrust chronosequence. Overall, our results suggest that the cumulative effects of altered precipitation frequencies on the stability of biocrusts will depend on biocrust maturity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Pangenomes suggest ecological-evolutionary responses to experimental soil warming

ABSTRACT Below-ground carbon transformations that contribute to healthy soils represent a natural climate change mitigation, but newly acquired traits adaptive to climate stress may alter microbial feedback mechanisms. To better define microbial evolutionary responses to long-term climate warming, we study microorganisms from an ongoing in situ soil warming experiment where, for over three decades, temperate forest soils are continuously heated at 5°C above ambient. We hypothesize that across generations of chronic warming, genomic signatures within diverse bacterial lineages reflect adaptations related to growth and carbon utilization. From our bacterial culture collection isolated from experimental heated and control plots, we sequenced genomes representing dominant taxa sensitive to warming, including lineages of Actinobacteria, Alphaproteobacteria, and Betaproteobacteria. We investigated genomic attributes and functional gene content to identify signatures of adaptation. Comparative pangenomics revealed accessory gene clusters related to central metabolism, competition, and carbon substrate degradation, with few functional annotations explicitly associated with long-term warming. Trends in functional gene patterns suggest genomes from heated plots were relatively enriched in central carbohydrate and nitrogen metabolism pathways, while genomes from control plots were relatively enriched in amino acid and fatty acid metabolism pathways. We observed that genomes from heated plots had less codon bias, suggesting potential adaptive traits related to growth or growth efficiency. Codon usage bias varied for organisms with similar 16S rrn operon copy number, suggesting that these organisms experience different selective pressures on growth efficiency. Our work suggests the emergence of lineage-specific trends as well as common ecological-evolutionary microbial responses to climate change. IMPORTANCE Anthropogenic climate change threatens soil ecosystem health in part by altering below-ground carbon cycling carried out by microbes. Microbial evolutionary responses are often overshadowed by community-level ecological responses, but adaptive responses represent potential changes in traits and functional potential that may alter ecosystem function. We predict that microbes are adapting to climate change stressors like soil warming. To test this, we analyzed the genomes of bacteria from a soil warming experiment where soil plots have been experimentally heated 5°C above ambient for over 30 years. While genomic attributes were unchanged by long-term warming, we observed trends in functional gene content related to carbon and nitrogen usage and genomic indicators of growth efficiency. These responses may represent new parameters in how soil ecosystems feedback to the climate system.

Choudoir, Mallory J. (ORCID:0000000291175150)↗
DOE OSTI2017

pH-Dependent Single-Step Rapid Synthesis of CuO Nanoparticles and Their Optical Behavior

This paper reports the pH-dependent growth of copper oxide (CuO) nanoparticles by wet chemical precipitation method using pH value of the samples as the only variable parameter. The phase purity, morphology, optical behavior, and elemental analysis of synthesized nanoparticles are shown to be critically dependent on the pH of the samples. Scanning electron microscope (SEM) results shows that a higher pH results well-defined CuO nanoflakes. X-ray powder diffraction (XRD) results disclosed that the growth of pure CuO with monoclinic structure at higher pH 8, whereas mixed phase was formed at lower pH 7. The average crystallite size of samples prepared at pH 8 to 10 was varying from 23.36 to 25.78 nm. The infrared spectroscopy showed that the O–H stretching peaks become narrow with an increase in the pH value. Optical results revealed the existence of the sharp absorption edges with precise excitonic features and photoluminescence bands both located at visible and near infrared spectral region attributed to the excellent optical behavior and narrow size distribution of particles. The additional near infrared band in photoluminescence spectrum located at 860 nm is attributed the defect-related luminescence. The growth mechanism of CuO nanostructures was discussed in the light of our findings.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Bimodality in Arrays of In{sub 0.4}Ga{sub 0.6}As Hybrid Quantum-Confined Heterostructures Grown on GaAs Substrates

Hybrid quantum-confined heterostructures grown by metal-organic vapor-phase epitaxy (MOVPE) via the deposition of In{sub 0.4}Ga{sub 0.6}As layers with various nominal thicknesses onto vicinal GaAs substrates are studied by photoluminescence spectroscopy and transmission electron microscopy. The photoluminescence spectra of these structures show the superposition of two spectral lines, which is indicative of the bimodal distribution of the size and/or shape of light-emitting objects in an array. The dominant spectral line is attributed to the luminescence of hybrid “quantum well–dot” nanostructures in the form of a dense array of relatively small quantum dots (QDs) with weak electron and hole localization. The second, lower intensity line is attributed to luminescence from a less dense array of comparatively larger QDs. Analysis of the behavior of the spectral line intensities at various temperatures showed that the density of larger QDs grows with increasing thickness of the InGaAs layer.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Plasmon Resonance Induced Photoconductivity in the Yttria Stabilized Zirconia Films with Embedded Au Nanoclusters

We report on the investigation of the photoconductivity (PC) in the yttria stabilized zirconia (YSZ) films with embedded Au nanoclusters (NCs). A peak in the PC spectrum corresponding to the plasmon optical absorption resonance in the Au NCs was observed. The temperature dependence of PC near 300 K obeyed Mott law. In this case, the PC was attributed to the heating of the YSZ matrix due to the plasmon optical absorption in the Au NCs (bolometric effect). Near 77 K, the PC did not depend on temperature and was attributed to plasmon-assisted electron transport between the NCs via the vacancy α-band in YSZ.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of thermochemical treatments on laser-induced luminescence spectra from strontium titanate: comparison with swift ion-beam irradiation experiments

Results recently reported on the effect of thermochemical treatments on the (He-Cd) laser-excited emission spectra of strontium titanate (STO) are re-analyzed here and compared with results obtained under ion-beam irradiation. Contributing bands centered at 2.4 eV and 2.8 eV, which appear under laser excitation, present intensities dependent upon previous thermal treatments in oxidizing (O 2 ) or reducing atmosphere (H 2 ). As a key result, the emission band centered at 2.8 eV is clearly enhanced in samples exposed to a reducing atmosphere. From a comparison with the ionoluminescence data, it is concluded that the laser-excited experiments can be rationalized within a framework developed from ion-beam excitation studies. In particular, the band at 2.8 eV, sometimes attributed to oxygen vacancies, behaves as expected for optical transitions from conduction-band (CB) states to the ground state level of the self-trapped exciton center. The band at 2.0 eV reported in ion-beam irradiated STO, and attributed to oxygen vacancies, is not observed in laser-excited crystals. As a consequence of our analysis, a consistent scheme of electronic energy levels and optical transitions can now be reliably offered for strontium titanate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

IRNet: A General Purpose Deep Risidual Regression Framework for Materials Discovery

Materials discovery is crucial for making scientific advances in many domains. Collections of data from experiments and first-principle computations have spurred interest in applying machine learning methods to create predictive models capable of mapping from composition and crystal structures to materials properties. Generally, these are regression problems with the input being a 1D vector composed of numerical attributes representing the material composition and/or crystal structure. While neural networks consisting of fully connected layers have been applied to such problems, their performance often suffers from the vanishing gradient problem when network depth is increased. Hence, predictive modeling for such tasks has been mainly limited to traditional machine learning techniques such as Random Forest. In this paper, we study and propose design principles for building deep regression networks composed of fully connected layers with numerical vectors as input. We introduce a novel deep regression network with individual residual learning, IRNet, that places shortcut connections after each layer so that each layer learns the residual mapping between its output and input. We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet’s performance against that of other machine learning techniques. Using multiple datasets from the Open Quantum Materials Database (OQMD) and Materials Project for training and evaluation, we show that IRNet provides significantly better prediction performance than the state-of-the-art machine learning approaches currently used by domain scientists. We also show that IRNet’s use of individual residual learning leads to better convergence during the training phase than when shortcut connections are between multi-layer stacks while maintaining the same number of parameters.

deep learning↗
DOE OSTI2026

Wattchmen: Watching the Wattchers – High Fidelity, Flexible GPU Energy Modeling

Modern GPU-rich HPC systems are increasingly becoming energy-constrained. Thus, understanding an application’s energy consumption becomes essential. Unfortunately, current GPU energy attribution techniques are either inaccurate, inflexible, or outdated. Therefore, we propose Wattchmen, a flexible methodology for measuring, attributing, and predicting GPU energy consumption. We construct a per-instruction energy model using a diverse set of microbenchmarks to systematically quantify the energy consumption of GPU instructions, enabling finer-grain prediction and energy consumption breakdowns for applications. Compared with the state-of-the-art systems like AccelWattch (32%) and Guser (25%), across 16 popular GPGPU, graph analytics, HPC, and ML workloads, Wattchmen reduces the mean absolute percent error (MAPE) to 14% on V100 GPUs. Furthermore, we show that Wattchmen provides similar MAPEs for water-cooled V100s (15%) and extends to later architectures, including air-cooled A100 (11%) and H100 (12%) GPUs. Finally, to further demonstrate Wattchmen ’s value, we apply it to applications such as Backprop and QMCPACK, where Wattchmen ’s insights enable energy reductions of up to 35%.

Tran, Brandon [University of Wisconsin, Madison] (↗
DOE OSTI2017

Synthesis and Characterization of Perfluorinated Anion Exchange Membranes

Anion exchange membranes (AEMs) are of high interest for a number of electrochemical device applications including fuel cells, electrolyzers, and flow batteries. Perfluorinated sulfonic acid polymers have been the standard polymer used in the much more established area of proton exchange membrane based devices due to specific advantageous attributes including chemical stability, high conductivity, high water mobility, and the ability to create high performance electrodes. These attributes would make for desirable AEMs, but synthesizing perfluorinated AEMs has been limited and has significant challenges. Here, we report our efforts to develop novel synthesis routes to sulfonamide-linked alkyl ammonium perfluorinated AEMs. We have demonstrated the ability to achieve both high levels of ion exchange and membrane conductivity. We have achieved improved durability by extending the length of the alkyl tether from 3 to 6 carbons, and we have demonstrated the ability to process these polymers into membranes, ionomer solutions/dispersions, and fuel cells with reasonable performance.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Understanding Li-Ion Cell Internal Short Circuit and Thermal Runaway through Small, Slow and In Situ Sensing Nail Penetration

Here we report a small, slow and in situ sensing (3S) nail penetration test method to understand Li-ion cell internal short circuit (ISC) and thermal runaway. The method not only keeps conventional nail penetration's advantage of simple implementation, but also enhances its relevance to field failures and enables detailed in situ diagnosis. It was applied to 3-Ah pouch cells and revealed insights that could not be captured by conventional methods. Most interestingly, multiple in situ temperature peaks were observed during a period of over 100 s before thermal runaway. These initial peaks exceeded safety limit but the temperature rapidly decreased after each peak instead of causing immediate thermal runaway. Further investigation suggested that the initial temperature peaks occurred when nail tip reached aluminum foil current collector to form a low resistance ISC between anode and aluminum foil. The rapid temperature decrease after each peak can be attributed to sudden drop of ISC current, which can be further attributed to rupture of aluminum foil and increase of contact resistance. The findings show that 3S nail penetration test can separate processes of ISC from thermal runaway and provide details of ISC at the level of individual electrode and current collector.

25 ENERGY STORAGE↗
DOE OSTI2022

A Right Transfer Access Control Model of Internet of Things Based on Smart Contract

Sensor nodes play a crucial role in the promotion of development of Internet of Things (IoT). Through this transaction, RO defines access control policies in script form based on ABAC's access control model to grant access right. The identity of all users in the model is identified by address. This paper builds a more flexible right transfer access control model by means of combining the Attribute-Based Access Control model (ABAC) and blockchain technology. Owing to the characteristics of ABAC’s attributes and right association, the massive problems of some sensor nodes can be solved. At the same time, for the sake of addressing the dynamic problems such as node access and right transfer, right transfer contract (TS) and access control contract (CS) are employed on the chain to ensure efficient and safe transmission of rights. To solve on-chain storage problems and ensure transparency of the operation, the idea of Rollup in Ethereum expansion is used to upload the final state of protocol policy and right exchange to the chain. Any user can know the policy and current right transfer status at any time. Finally, comparative and security analysis show that the model presented here can solve IoT devices’ massive and dynamic problems more effectively and it is more secure than the traditional models.

Wang, Jiuru↗
DOE OSTI2019

Nonlinear saturation and oscillations of collisionless zonal flows

In homogeneous drift-wave (DW) turbulence, zonal flows (ZFs) can be generated via a modulational instability (MI) that either saturates monotonically or leads to oscillations of the ZF energy at the nonlinear stage. This dynamics is often attributed as the predator-prey oscillations induced by ZF collisional damping; however, similar dynamics is also observed in collisionless ZFs, in which case a different mechanism must be involved. Here, we propose a semi-analytic theory that explains the transition between the oscillations and saturation of collisionless ZFs within the quasilinear Hasegawa-Mima model. By analyzing phase-space trajectories of DW quanta (driftons) within the geometrical-optics (GO) approximation, we argue that the parameter that controls this transition is N ~ \gamma_MI/\omega_DW, where \gamma_MI is the MI growth rate and \omega_DW is the linear DW frequency. We argue that at N << 1, ZFs oscillate due to the presence of so-called passing drifton trajectories, and we derive an approximate formula for the ZF amplitude as a function of time in this regime. We also show that at N >~ 1, the passing trajectories vanish and ZFs saturate monotonically, which can be attributed to phase mixing of higher-order sidebands. A modification of N that accounts for effects beyond the GO limit is also proposed. These analytic results are tested against both quasilinear and fully-nonlinear simulations. They also explain the earlier numerical results by Connaughton et al. [J. Fluid Mech. 654, 207 (2010)] and Gallagher et al. [Phys. Plasmas 19, 122115 (2012)] and offer a revised perspective on what the control parameter is that determines the transition from the oscillations to saturation of collisionless ZFs.

collisionless zonal flows, modulational instabilit↗
DOE OSTI2022

Tomo2Mesh: Fast Reconstruction and Visualization of Tomography Data in Mesh Format

Tomo2Mesh is an open-source project targeted towards real-time reconstruction, segmentation, and visualization of computed tomography (CT) data in mesh format. The CT reconstruction scheme is based on filtered back-projection of voxel subsets. The segmentation scheme uses a 3D convolutional neural network. To allow for fast, real-time reconstruction, voxel subsets are first identified by coarse reconstruction. The detail in specific regions of interest is improved through full reconstruction of voxel subsets in that region. Data structures are implemented to store and process voxel subsets. Finally, voxel data is labeled using connected components to detect disconnected regions such as voids whose morphological attributes (e.g., Feret diameter, principal axis orientation, local number density, size, etc.) can be measured also in real-time. Finally, a fast marching cubes implementation processes labeled voxel data into a triangular face mesh (vertices and faces) in .ply format for visualization in Paraview or other mesh visualization tools. The code provides a simple programming interface for detecting, classifying, and visualizing regions of interest based on morphology. For example, detected voids can be classified as round pores or extended cracks. Highly porous neighborhoods can be identified based on local number density. The mesh texture (or color) is assigned based these morphological attributes to allow smart visualization scenarios in real-time (e.g., show only long cracks). At the time of first release (July 2022), extraction of face mesh for visualization for raw CT data from a 2 megapixel camera would take between 1-5 minutes for most scenarios.

TEKAWADE, ANIKET↗
DOE OSTI2019

Hurricane Harvey Flood

Online supplement to Attributable human-induced changes in the magnitude of flooding in the Houston, Texas region during Hurricane Harvey, by Michael Wehner and Christopher Sampson. Geospatial data to visualize the attributable flooding in the greater Houston during Hurricane Harvey as simulated by the Fathom hydraulical model in a geotiff file format is provided at https://portal.nersc.gov/cascade/Harvey/ . The public is encouraged to download this open access data and investigate their own neighborhoods of interest using free or commercial software. A detailed validation of the baseline simulated "flood that was" for every neighborhood is outside the scope of this article but individuals can compare the simulation to their own experiences. The public is then invited to compare the "flood that might have been" had there not been a human interference in the climate system to the simulated actual flood in these neighborhoods. The variety of scientific opinions as to the human effect on precipitation and their effect on the flood are presented as detailed in the table below. A pair of high risk scenarios of the effect of a future warmer climate and a hypothetical storm similar to Harvey is also provided.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

RAIS Slope Factors for Radionuclides

For a given radionuclide, Slope Factors (SFs) represent the ELCR equivalent per unit intake (i.e., ingestion or inhalation) or external exposure of that radionuclide. These SFs are used to convert a radionuclide concentration in soil, air, water, or foodstuffs to a radiation ELCR. The primary use of SFs, also called risk coefficients, is to compute the ELCR resulting from site-related exposures. This is accomplished by multiplying the route-specific SF by the CDI of each radionuclide of potential concern for each route of exposure. Ingestion and inhalation slope factors are central estimates in a linear model of the age-averaged, lifetime attributable radiation cancer incidence (fatal and nonfatal cancer) risk per unit of activity inhaled or ingested, expressed as risk/picocurie (pCi). External exposure SFs are central estimates of lifetime attributable radiation cancer incidence risk for each year of exposure to external radiation from photon-emitting radionuclides distributed uniformly in a thick layer of soil, expressed as risk/year per pCi/gram soil. External exposure SF units can also be risk/year per pCi/square centimeters (cm2) of soil. When combined with site-specific media concentration data and appropriate exposure assumptions, SFs can be used to estimate lifetime cancer risks to members of the general population due to radionuclide exposures. The SFs presented on are updated values from Federal Guidance Report (FGR) 13 supplement using the International Commission on Radiological Protection (ICRP) 107 decay data. The derivation and values are found in “Calculations of Slope Factors and Dose Coefficients” (ORNL, 2014).

Noto, Katie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part I: Observational Study

A study of the vertical structure of postfrontal shallow clouds in the marine boundary layer over the Southern Ocean is presented here. The central question of this two-part study regards cloud phase (liquid/ice) of precipitation, and the associated growth mechanisms. In this first part, data from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign are analyzed, starting with a 75-h case with continuous sea surface-based thermal instability, modest surface heat fluxes, an open-cellular mesoscale organization, and very few ice nucleating particles (INPs). The clouds are mostly precipitating and shallow (tops mostly around 2 km above sea level), with weak up- and downdrafts, and with cloud-top temperatures generally around –18° to –10°C. Further, the case study is extended to three other periods of postfrontal shallow clouds in MARCUS. While abundant supercooled liquid water is commonly present, an experimental cloud-phase algorithm classifies nearly two-thirds of clouds in the 0° to –5°C layer as containing ice (cloud ice, snow, or mixed phase), implying that much of the precipitation grows through cold-cloud processes. The best predictors of ice presence are cloud-top temperature, cloud depth, and INP concentration. Measures of convective activity and turbulence are found to be poor indicators of ice presence in the studied environment. The water-phase distribution in this cloud regime is explored through numerical simulations in Part II. Significance Statement: Climate models generally predict a lower albedo than observed over the Southern Ocean, and this is largely attributed to a lack of cloudiness, especially in the postfrontal cold sector of midlatitude cyclones. This in turn may be due to an excess of ice in these simulated clouds, resulting in rapid precipitation fallout and an overly brief cloud lifespan. The objective of this study is to examine whether shallow postfrontal clouds over the Southern Ocean are dominated by supercooled drops, or by snow and ice, using data collected by a U.S. Department of Energy Atmospheric Radiation Measurement Mobile Facility deployed aboard an Australian Antarctic supply vessel. We find that these clouds contain much supercooled liquid, even though cloud-top temperatures generally are around –18° to –8°C, and that about two-thirds of the clouds just above the freezing level contain ice. Much of the precipitation appears to grow through cold-cloud processes above the freezing level, rather than drizzle/rain. Updrafts and/or turbulence in convection or in cloud-top generating cells do not initiate much ice, compared to observations elsewhere in a similar temperature range. This may be attributable to the extremely low concentration of ice nucleating particles in this environment. Ultimately, the deepest clouds with the coldest cloud tops are most likely to be ice dominated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Are 100 Ensemble Members Enough to Capture the Remote Atmospheric Response to +2°C Arctic Sea Ice Loss?

This study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two general circulation models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100 ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the midlatitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean–atmosphere PAMIP experiments are analyzed. Substantial differences in the midlatitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean–atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s t and false discovery rate tests. This is problematic for the multimodel assessment of the response, as some of the spread may be attributed to different model sensitivities whereas it is due to internal variability. Here, we propose a method to overcome this consistency issue that allows for more robust conclusions when only 100 ensemble members are used.

58 GEOSCIENCES↗