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DOE OSTI2024

MISIP: a data standard for the reuse and reproducibility of any stable isotope probing-derived nucleic acid sequence and experiment

DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields—isotope, isotopolog, isotopolog label, labeling approach, and gradient position—and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.

Simpson, Abigayle↗

DOE OSTI2022

Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target

Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4‰ at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 ± 1.2 Mg C ha -1 year -1 to increase SOC stocks by 4‰ per year over 30 years, compared to the C input in the control treatments (multi-model median ± median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Here, our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition.

4 per 1000 initiative↗

DOE OSTI2023

PhytoOracle: Scalable, modular phenomics data processing pipelines

As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to ( i ) improve data processing efficiency; ( ii ) provide an extensible, reproducible computing framework; and ( iii ) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Understanding Reproducibility of Sputter‐Deposited Metastable Ferroelectric Wurtzite Al 0.6 Sc 0.4 N Films Using In Situ Optical Emission Spectrometry

High‐Sc Al 1– x Sc x N thin films are of tremendous interest because of their attractive piezoelectric and ferroelectric properties, but overall film quality and reproducibility are widely reported to suffer as x increases. In this study, structural and electrical properties of metastable Al 0.6 Sc 0.4 N films are connected with plasma changes during film growth, identified via glow discharge optical emission spectroscopy (GD‐OES), and linked to the target mode changes. This in situ GD‐OES technique uses changes in the N 2 (I) intensity, correlated with DC bias hysteresis behavior of a Al 0.6 Sc 0.4 target in metallic and poisoned modes, to identify films that subsequently exhibit unacceptable structural and electrical performance. Two representative samples deposited under identical conditions but possessing distinct properties related to phases present in the films are focused on. Films sputtered under a poisoned target mode produce pure wurtzite ferroelectric Al 0.6 Sc 0.4 N with a reversible 80 μC cm −1 polarization and 3.1 MV cm −1 coercive field. When identical chamber settings are used but the process starts in metallic mode, a mixed wurtzite/rocksalt film is deposited which exhibits nanometer‐scale changes to the film microstructure and a nonferroelectric response. These results illustrate the utility of optical emission spectroscopy for tracking target mode fluctuations when fabricating metastable materials such as high‐Sc Al 1– x Sc x N films.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Global fire modelling and control attributions based on the ensemble machine learning and satellite observations

Contemporary fire dynamics is one of the most complex and least understood land surface phenomena. Global fire controls related to climate, vegetation, and anthropogenic activity are usually intertwined, and difficult to disentangle in a quantitative way. Here, we leveraged an ensemble of five machine learning (ML) models and multiple satellite-based observations to conduct global fire modeling for three fire metrics (burned area, fire number, and fire size), and quantified driving mechanisms underlying annual fire changes in a spatially resolved manner for the period 2003–2019. Ensemble learning is a meta-approach that combines multiple ML predictions to improve accuracy, robustness, and generalization performance. We found that the optimized ensemble ML well reproduced annual dynamics of global burned area (R 2 = 0.90, P < 0.001), total fire numbers (R 2 = 0.86, P < 0.001), and averaged fire size (R 2 = 0.70, P < 0.001). Additionally, the ensemble ML captured key spatial patterns of multi-year mean magnitudes, annual variabilities, anomalies, and trends for different fire metrics. Our ML-based fire attributions further highlighted the dominant role of enhanced anthropogenic activity in reducing global burned area (–1.9 Mha/yr, P < 0.01), followed by climate control (–1.3 Mha/yr, P < 0.01) and insignificant positive vegetation control (0.4 Mha/yr, P = 0.60). Spatially, climate dominated a much larger burned area (53.7%) than human (23.4%) or vegetation control (22.9%); however, the counteracting effects from regional wetting and drying trends weakened the net climate impacts on global burned area. The fire number and fire size exhibited similar spatial control patterns with burned area; globally, however, fire number tended to be more affected by climate while fire size more influenced by human activities. Overall, our study confirmed the feasibility and efficiency of ensemble ML in global fire modeling and subsequent control attributions, providing a better understanding of contemporary fire regimes and contributing to robust fire projections in a changing environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A Model Intercomparison Study of Aerosol‐Cloud‐Turbulence Interactions in a Cloud Chamber: 1. Model Results

This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information

Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility. Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson’s R and Spearman’s ρ coefficients as well as Kendall’s τ and Top–Down correlation. We also test the behavior of association measures, including the coefficient of determination R 2 , Kendall’s W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spear man’s ρ, Kendall’s τ, and Kendall’s W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R 2 coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships. Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Understanding the TiH (2-x) /TiO y System at Elevated Temperature: A Literature Review

Titanium hydride of varying TiH stoichiometry is used in pyrotechnic compositions. In order to yield consistent performance, manufacturing processes must be developed to ensure precise and reproducible material properties, including composition and morphology. Legacy synthesis protocols are not comprehensive nor are the required apparatuses still available. To guide the development of novel production procedures, this report reviews literature on relevant chemical reactions and diffusion events occurring at elevated temperature in the TiH (2-x) /TiO y system. Titanium hydride exposed to air spontaneously forms a passivating oxide layer. Upon heating, significant hydrogen release, which is accompanied by changes to the surface oxide layer, is noted by 375–400°C. At higher temperatures (above about 500°C) the oxide layer is reported to be essentially nonexistent as a result of oxide-layer dissolution processes and, potentially, oxide-layer reduction due to water formation. Based on the reviewed literature, we hypothesize that, by 500°C, the surface layer consists of an oxyhydride phase, which is a solid solution of oxygen in titanium hydride. We believe that hydrogen release from titanium hydride is controlled by the kinetics of molecular hydrogen desorption on the oxyhydride surface. No literature data is available for corresponding activation energies of the dynamic desorption process, and the equilibrium phase diagram of this three-component system remains largely unexplored as well. These gaps in knowledge might be addressed through coordinated computational modeling and experimental efforts.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Suppressing Co-Crystallization of Halogenated Non-Fullerene Acceptors for Thermally Stable Ternary Solar Cells

While photovoltaic blends based on non-fullerene acceptors are touted for their thermal stability, this type of acceptor tends to crystallize, which can result in a gradual decrease in photovoltaic performance and affects the reproducibility of the devices. Two halogenated indacenodithienothiophene-based acceptors that readily co-crystallize upon mixing are studied, which indicates that the use of an acceptor mixture alone does not guarantee the formation of a disordered mixture. The addition of the donor polymer to the acceptor mixture readily suppresses the crystallization, which results in a fine-grained ternary blend with nanometer-sized domains that do not coarsen due to a high T g ≈ 200 °C. As a result, annealing at temperatures of up to 170 °C does not markedly affect the photovoltaic performance of ternary devices, in contrast to binary devices that suffer from acceptor crystallization in the active layer. The results indicate that the ternary approach enables the use of high-temperature processing protocols, which are needed for upscaling and high-throughput fabrication of organic solar cells. Further, ternary devices display a stable photovoltaic performance at 130 °C for at least 205 h, which indicates that the use of acceptor mixtures allows to fabricate devices with excellent thermal stability.

14 SOLAR ENERGY↗

DOE OSTI2025

Molten Salt Corrosion Tests of Additively Manufactured Stainless Steel 316H

Molten salt reactors (MSRs) have drawn considerable interest due to their favorable safety features, high thermal efficiency, and compatibility with different fuel cycles. Yet, the success of MSRs hinges critically on the performance of structural materials to be used in these aggressive molten salt environments, where corrosion and material compatibility remain primary challenges to long-term reliability. Additively manufactured (AM) nuclear structural materials prompt the use of novel geometries and compositions to enhance material performance and reduce costs of constructing MSRs. The rapid solidification conditions inherent to AM processing impart distinctive microstructural features, including cellular sub-structures, dislocation densities, residual stress, and oxide inclusions, which can influence material performance in MSR components. While the mechanical properties of AM stainless steels have been widely studied, their corrosion behavior, particularly in molten salt environments, has received far less attention. Addressing these needs, the Advanced Materials and Manufacturing Technologies (AMMT) program provides a framework for systematically evaluating how unique microstructures produced by AM processes influence the performance of these materials in these demanding environments and for developing reproducible testing workflows that can support future code qualification efforts and standards development. Bridging this knowledge gap is essential for assessing the viability of AM alloys in MSRs and informing qualification strategies. A further challenge is the absence of standardized protocols for molten salt corrosion testing. Accordingly, this report provides an account of the corrosion evaluation of AM 316H stainless steel in NaCl 2 -MgCl 2 molten salt at 550 °C, with exposure times of 100 and 500 hours. It documents the experimental procedures implemented under the AMMT program, including salt preparation, exposure protocols, and post-test characterization methods, to establish reproducibility and transparency. Importantly, the study examines AM 316H samples in the as-fabricated condition, directly reflecting the surface state most relevant to engineering applications, and compares their behavior to machine-cut surfaces. Overall, preliminary evaluations have noted that surface conditions (e.g. morphology, contamination, etc.) have a noticeable impact on the corrosion resiliency. The impact of the corrosion is difficult to detect at 100h, unless, in the case of AM 316H, the specimen surface is decontaminated. After 500 h, as-fabricated surfaces of AM and wrought 316H display evidence of general versus preferential corrosion attack, respectively. Both AM and wrought 316H machine-cut surfaces exhibit a continuous Cr depletion zone, evident of general corrosion. While the estimated extent of corrosion appears within the same order of magnitude regardless of the surface condition, it is apparent that more predictable behavior is observed on machine-cut surfaces. Nonetheless, further investigation is necessary to fully elucidate the corrosion mechanism under these conditions.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Pseudogap in electron-doped cuprates: Strong correlation leading to band splitting

The pseudogap phenomena have been a long-standing mystery of the cuprate high-temperature superconductors. The pseudogap in the electron-doped cuprates has been attributed to band folding due to antiferromagnetic (AFM) long-range order or short-range correlation. We performed an angle-resolved photoemission spectroscopy study of the electron-doped cuprates Pr 1.3−x La 0.7 Ce x CuO 4 showing spin-glass, disordered AFM behaviors, and superconductivity at low temperatures and, by measurements with fine momentum cuts, found that the gap opens on the unfolded Fermi surface rather than the AFM Brillouin zone boundary. The gap did not show a node, following the full symmetry of the Brillouin zone, and its magnitude decreased from the zone-diagonal to (π,0) directions, opposite to the hole-doped case. These observations were reproduced by cluster dynamical-mean-field-theory calculation, which took into account electron correlation precisely within a (CuO 2 ) 4 cluster. In conclusion, the present experimental and theoretical results are consistent with the mechanism that electron or hole doping into a Mott insulator creates an in-gap band that is separated from the upper or lower Hubbard band by the pseudogap.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Impact of solvent forces and broken symmetry on the assembly of designed proteins at a liquid-solid interface

The era of protein design has enabled the creation of hybrid protein-inorganic interfaces, leading to both surface-directed self-assembly of de novo protein architectures and protein-directed formation of inorganic materials. However, the resulting patterns of protein assembly are often unexpected, implying that essential interactions are not accounted for in current design platforms. Here, we use high-speed atomic force microscopy (AFM) analyzed through machine learning to follow the assembly of protein nanorods in aqueous electrolytes on two types of mica exhibiting disparate symmetry elements, which are imprinted on the overlying hydration structure. Using Monte Carlo simulations, we reproduce the observed phases and show that an observed smectic phase, previously thought to be unstable for non-interacting rods in two dimensions, emerges when crystal symmetry introduces a directional bias. The findings demonstrate the importance of incorporating solvent forces as modulated by the hydration structure inherent to interfacial systems when designing protein assemblies at liquid-crystal interfaces. Coupling physics-based simulations that can account for these factors to de novo protein design algorithms can lead to improved design platforms for bio-inspired, hybrid materials.

bioinspired materials↗

DOE OSTI2019

Nonequilibrium physics in biology

Life is characterized by a myriad of complex dynamic processes allowing organisms to grow, reproduce, and evolve. Physical approaches for describing systems out of thermodynamic equilibrium have been increasingly applied to living systems, which often exhibit phenomena not found in those traditionally studied in physics. Spectacular advances in experimentation during the last decade or two, for example, in microscopy, single-cell dynamics, in the reconstruction of subcellular and multicellular systems outside of living organisms, and in high throughput data acquisition, have yielded an unprecedented wealth of data on cell dynamics, genetic regulation, and organismal development. These data have motivated the development and refinement of concepts and tools to dissect the physical mechanisms underlying biological processes. Notably, landscape and flux theory as well as active hydrodynamic gel theory have proven useful in this endeavor. Together with concepts and tools developed in other areas of nonequilibrium physics, significant progress has been made in unraveling the principles underlying efficient energy transport in photosynthesis, cellular regulatory networks, cellular movements and organization, embryonic development and cancer, neural network dynamics, population dynamics and ecology, as well as aging, immune responses, and evolution. In this paper, recent advances in nonequilibrium physics are reviewd and their application to biological systems is surveyed. Many of these results are expected to be important cornerstones as the field continues to build our understanding of life.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Effective radiative forcing of anthropogenic aerosols in E3SM version 1: historical changes, causality, decomposition, and parameterization sensitivities

Abstract. The effective radiative forcing of anthropogenic aerosols (ERFaer) is an important measure of the anthropogenic aerosol effects simulated by a global climate model. Here we analyze ERFaer simulated by the E3SM version 1 (E3SMv1) atmospheric model using both century-long free-running atmosphere–land simulations and short nudged simulations. We relate the simulated ERFaer to characteristics of the aerosol composition and optical properties, and we evaluate the relationships between key aerosol and cloud properties. In terms of historical changes from the year 1870 to 2014, our results show that the global mean anthropogenic aerosol burden and optical depth increase during the simulation period as expected, but the regional averages show large differences in the temporal evolution. The largest regional differences are found in the emission-induced evolution of the burden and optical depth of the sulfate aerosol: a strong decreasing trend is seen in the Northern Hemisphere high-latitude region after around 1970, while a continued increase is simulated in the tropics. The relationships between key aerosol and cloud properties (relative changes between pre-industrial and present-day conditions) also show evident changes after 1970, diverging from the linear relationships exhibited for the period of 1870–1969. In addition to the regional differences in the simulated relationships, a reduced sensitivity in cloud droplet number and other cloud properties to aerosol perturbations is seen when the aerosol perturbation is large. Consequently, the global annual mean ERFaer magnitude does not increase after around 1970. The ERFaer in E3SMv1 is relatively large compared to the recently published multi-model estimates; the primary reason is the large indirect aerosol effect (i.e., through aerosol–cloud interactions). Compared to other models, E3SMv1 features large relative changes in the cloud droplet effective radius in response to aerosol perturbations. Large sensitivity is also seen in the liquid cloud optical depth, which is determined by changes in both the effective radius and liquid water path. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 are found to have a strong correlation, as the evolution of anthropogenic sulfate aerosols affects both the liquid cloud formation and the homogeneous ice nucleation in cirrus clouds (that causes a large effect on longwave ERFaer). As suggested by a previous study, the large ERFaer appears to be one of the reasons why the model cannot reproduce the observed global mean temperature evolution in the second half of the 20th century. Sensitivity simulations are performed to understand which parameterization and/or parameter changes have a large impact on the simulated ERFaer. The ERFaer estimates in E3SMv1 for the shortwave and longwave components are sensitive to the parameterization changes in both liquid and ice cloud processes. When the parameterization of ice cloud processes is modified, the top-of-model forcing changes in the shortwave and longwave components largely offset each other, so the net effect is negligible. This suggests that, to reduce the magnitude of the net ERFaer, it would be more effective to reduce the anthropogenic aerosol effect through liquid or mixed-phase clouds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Promoting the regulatory acceptance of combined ion and neutron irradiation for material degradation in nuclear reactors

The Advanced Materials and Manufacturing Technologies (AMMT) program within the Department of Energy (DOE) Office of Nuclear Energy has developed its current recommendation for promoting the use of combined ion irradiation and neutron irradiation for the accelerated qualification of materials to be deployed in nuclear reactors. This plan is intended to provide a collaborative path forward that can be adopted by academia, national laboratories, and industry, and has been developed with input from the regulatory research arm of the U.S. Nuclear Regulatory Commission (NRC). To deploy new materials or materials manufactured with new technologies, such as additive manufacturing, materials must be evaluated for reactor-induced degradation from the combination of harsh temperatures, corrosive environments, and radiation fields. However, rapid deployment of materials necessitates accelerated testing methods rather than relying on years of neutron irradiation in a material test reactor. Ion irradiation has demonstrated success in reproducing material microstructure and select property evolution resulting from neutron irradiation with three to four orders of magnitude reduction in time and cost, making it an ideal candidate for accelerated irradiation testing. This presentation provides context governing both the scientific and regulatory aspects of the proposed goal. The discussion is aimed at a broad audience including researchers from industry, national laboratories, and academia. The recommended path forward is presented as a conceptual framework of specific steps. In brief, the strategy entails developing an integrated ion and neutron irradiation test plan for the material property of interest based on the fundamental tenet of the linkage of microstructure and properties in materials. Physics-based modeling interprets ion irradiation data and predicts neutron irradiation microstructure and properties with uncertainty bounds. The first round of testing is sufficient for an initial licensing application using a risk-informed approach, while a minimum required neutron irradiation test plan reduces cost and time requirements. A surveillance program with witness specimens in-reactor provides additional data over time to improve model predictions to higher damage levels and further reduce uncertainty bounds, which can be used for license extensions or longer lifetimes in new license applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

A Global High‐Resolution Mesoscale Convective System Database Using Satellite‐Derived Cloud Tops, Surface Precipitation, and Tracking

Abstract A new methodology is developed to construct a global (60°S–60°N) long‐term (2000–2019) high‐resolution (∼10‐km h) mesoscale convective system (MCS) database by tracking MCS jointly using geostationary satellite infrared brightness temperature ( T b ) and precipitation feature (PF) characteristics from the Integrated Multi‐satellitE Retrievals for GPM precipitation data sets. Independent validation shows that the satellite‐based MCS data set is able to reproduce important MCS statistics derived from ground‐based radar network observations in the United States and China. We show that by carefully considering key PF characteristics in addition to T b signatures, the new method significantly improves upon previous T b ‐only methods in detecting MCSs in the midlatitudes for all seasons. Results show that MCSs account for over 50% of annual total rainfall across most of the tropical belt and in selected regions of the midlatitudes, with a strong seasonality over many regions of the globe. The tracking database allows Lagrangian aspects such as MCS lifetime and translational speed and direction to be analyzed. The longest‐lived MCSs preferentially occur over the subtropical oceans. The land MCSs have higher cloud‐tops associated with more intense convection, and oceanic MCSs have much higher rainfall production. While MCSs are observed in many regions of the globe, there are fundamental differences in their dynamic and thermodynamic structures that warrant a better understanding of processes that control their evolution. This global database provides significant opportunities for observational and modeling studies of MCSs, their characteristics, and roles in regional and global water and energy cycles, as well as their hydrologic and other impacts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Large Ensemble Particle Filter for Spatial Climate Reconstructions Using a Linear Inverse Model

Proxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) Particle filter (PF) that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR PF. Our results show that the LIMs allow for skillful ensemble forecasts at 1-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM-emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR PF.

54 ENVIRONMENTAL SCIENCES↗