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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dysfunctionality Hazard Curve: Risk-Based Tool to Support the Resilient Design of Systems Subjected to Multihazards

While resilience metrics have been proposed and studied given a functionality recovery curve, they have not emphasized enough on accounting for the uncertainties in the multihazard occurrences and intensities. Moreover, these resilience metrics are not risk-based (i.e., they do not express the system's resilience loss as a frequency of exceedance), leading to inconsistencies in system performance description when compared to performance-based engineering frameworks. A risk-based tool termed dysfunctionality hazard curve is proposed to assess the resilience of systems subjected to single hazards or multihazards with inter-event dependencies. Dysfunctionality hazard curve expresses system resilience performance as frequency of exceedance of time to full functionality. In doing so, it characterizes system recovery as a sequence of repair activities and also considers the uncertainties in the multihazard occurrences and intensities. Dysfunctionality hazard curve is demonstrated for a residential building susceptible to earthquake and hurricane hazards. Results indicate that Dysfunctionality hazard curve for earthquakes is greater than that for hurricane winds under single hazards. Under multihazards, considering inter-event dependencies during system recovery leads to larger dysfunctionality hazard curve than ignoring them. Finally, the concept of dysfunctionality hazard curve is also extended to a system-of-systems consisting of residential and commercial buildings.

42 ENGINEERING↗
DOE OSTI2020

BioSTEAM-LCA: An Integrated Modeling Framework for Agile Life Cycle Assessment of Biorefineries under Uncertainty

Biorefineries will play a critical role in sustainable bioeconomies, but projections of their environmental impacts vary widely. A core challenge with life cycle assessments (LCAs) of biorefineries is that they are often disconnected from biorefinery design, simulation, and techno-economic analysis (TEA). This lack of integration is a barrier to early stage technology and process evaluations, reducing consistency and transparency across sustainability indicators while limiting our understanding of the relative importance of individual factors (e.g., design decisions, greenhouse gas emission accounting procedures), how these factors interact, and trade-offs or synergies with process economics. In this study, we propose a new agile LCA framework, BioSTEAMLCA, which layers onto BioSTEAM (Biorefinery Simulation and Techno-Economic Analysis Modules, which automates biorefinery design, simulation, and TEA) to characterize the environmental impacts of biorefineries across a landscape of designs, technology performance assumptions, and contexts. Inventory databases and impact assessment methods are integrated to enable flexible user defined LCA system models, and the implications of uncertainties throughout the production system are characterized via Monte Carlo simulation. To demonstrate the capabilities of BioSTEAMLCA, we present a case study for sugarcane ethanol production. Overall, BioSTEAM-LCA enables computationally efficient, agile gate-to-gate LCA to evaluate biorefinery processes, the production of candidate biofuels and bioproducts, and trade-offs among productivity, economics, and environmental impacts under uncertainty.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Particle Markov Chain Monte Carlo Approach to Inference in Transient Surface Kinetics

Here, in this work, we develop a novel Bayesian approach to study the adsorption and desorption of CO onto a Pd(111) surface, a process of great importance in natural sciences. The motivation for this work comes from the recent availability of time-resolved infrared spectroscopy data and the need for model interpretability and uncertainty quantification in chemical processes. The objective is to learn the relevant parameters that characterize the process: coverage with time, rate constants, activation energies, and pre-exponential factors. Our approach consists of three main schemes: (i) a problem design and probabilistic model for the whole system, (ii) a particle Markov chain Monte Carlo sampler to learn the hidden coverages and rate constant parameters, and (iii) two Bayesian formulations to infer the activation energies and pre-exponential factors. The flexibility of the Bayesian framework allows for uncertainty quantification where possible and integration of mathematical constraints in the model to reflect the system physically. We found that our results for the activation energies and pre-exponential factor are in agreement with those reported in the experimental literature, independently, and we provide discussions on the advantages and disadvantages as well as applicability to other systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Completion of Transport Property Measurements on Multiple Actinide Fluoride Mixtures

One of the missions of the US Department of Energy’s Office of Nuclear Energy (DOE-NE) Molten Salt Reactor (MSR) Campaign under the Advanced Reactor Technology program has been to experimentally measure thermophysical properties of MSR-relevent salt systems, with the intent of supporting the development of the Molten Salt Thermal Properties Database (MSTDB). This database is jointly funded by the DOE-NE Nuclear Energy Advanced Modeling and Simulation Program and the MSR Campaign. Multiple DOE national laboratories, including Oak Ridge National Laboratory (ORNL), have been conducting measurements of thermophysical properties to support MSTDB development and provide MSR developers with access to new data that has been measured using modern methodologies and more advanced sample characterization techniques. These data may either fill gaps in the database or provide updated higher quality data to replace legacy data. Researchers at ORNL have recognized significant gaps in the transport property data of actinide-bearing fluoride salt systems of MSR industry interest. Moreover, for the data present in MSTDB in this category, the uncertainty margins are generally high, leading to questionability in our current understanding of the thermophysical characterization of actinide fluoride mixtures. As such, the focus of this study has been to generate new transport property data of actinide fluoride mixtures that are of immediate interest to MSR developers. Specifically, the mixtures NaF-UF 4 (78 - 22 mol%) and NaF-KF-UF 4 (57-16.04-26.91 mol%) have been studied—NaF-UF 4 for thermal conductivity and viscosity and NaF-KF-UF 4 for viscosity. Thermal conductivity measurements have been conducted with a variable gap apparatus, whereas viscosity has been measured with a rolling ball viscometer. Methodological and calibration details are provided for both measurement processes, along with measurement system updates that have enabled easier manufacturing of components and fewer challenges associated with conducting the measurements themselves. The resultant data collected for NaF-UF 4 (78–22 mol%) and NaF-KF-UF 4 (57-16.04-26.91 mol%) have been compared with relevant mixture data within the thermophysical arm of the MSTDB (MSTDB-TP).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Development and application of marginal likelihood optimization for integral parameter adjustment

When adjusting nuclear data with integral experiments, care must be taken that spurious adjustments are not made by assimilating poorly characterized integral parameters. If there are unaccounted for biases or poorly estimated uncertainties in the calculated and experimental values for an integral parameter, the Bayesian data assimilation may adjust the nuclear data in a manner that does not reflect the physics of the integral parameter. To identify and lessen the impact of these inconsistent integral parameters, in this study we present a Marginal Likelihood Optimization algorithm. In a data-driven way, the marginalized likelihood is used to modulate hyperparameter terms that decrease the influence of inconsistent integral parameters on the adjustment. The advantage of this approach over other methods in the literature is that it incorporates correlation information and does not remove an integral parameter from the adjustment. Herein, we present and motivate the algorithm, and apply it to an integral data assimilation case study.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Bayesian analysis of nuclear deformation properties with Skyrme energy functionals

In spite of numerous scientific and practical applications, there is still no comprehensive theoretical description of the nuclear fission process based solely on protons, neutrons and their interactions. Furthermore, the most advanced simulations of fission are currently carried out within nuclear density functional theory (DFT). In spite of being fully quantum-mechanical and rooted in the theory of nuclear forces, DFT still depends on a dozen or so parameters characterizing the energy functional. Calibrating these parameters on experimental data results in uncertainties that must be quantified for applications. This task is very challenging because of the high computational cost of DFT calculations for fission. In this paper, we use Gaussian processes to build emulators of DFT models in order to quantify and propagate statistical uncertainties of theoretical predictions for a range of nuclear deformations relevant to describing the fission process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Data-Driven Nonparametric Approach for Probabilistic Load-Margin Assessment Considering Wind Power Penetration

A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, here we propose a cost-effective, data-driven approach to assessing a power system's load margin probabilistically. Using actual wind data, a kernel density estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of traditional Monte-Carlo simulations that process a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian-learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate, which serves as a highly efficient, parametric Bayesian prior, is developed. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Results of simulations that are performed on several test systems reveal the impressive performance of the proposed method in the probabilistic load-margin assessment.

17 WIND ENERGY↗
DOE OSTI2020

XANES spectroscopy of sulfides stable under reducing conditions

X-ray absorption near-edge structure (XANES) spectroscopy is a powerful technique to quantitatively investigate sulfur speciation in geologically complex materials such as minerals, glasses, soils, organic compounds, industrial slags, and extraterrestrial materials. This technique allows non-destructive investigation of the coordination chemistry and oxidation state of sulfur species ranging from sulfide (2–oxidation state) to sulfate (6+ oxidation state). Each sulfur species has a unique spectral shape with a characteristic K-edge representing the s → p and d hybridization photoelectron transitions. As such, sulfur speciation is used to measure the oxidation state of samples by comparing the overall XANES spectra to that of reference compounds. Although many S XANES spectral standards exist for terrestrial applications under oxidized conditions, new sulfide standards are needed to investigate reduced (oxygen fugacity, f O 2 , below IW) silicate systems relevant for studies of extraterrestrial materials and systems. Sulfides found in certain meteorites (e.g., enstatite chondrites and aubrites) and predicted to exist on Mercury, such as CaS (oldhamite), MgS (niningerite), and FeCr 2 S 4 (daubréelite), are stable at f O 2 below IW-3 but rapidly oxidize to sulfate and/or produce sulfurous gases under terrestrial surface conditions. XANES spectra of these compounds collected to date have been of variable quality, possibly due to the unstable nature of certain sulfides under typical (e.g., oxidizing) laboratory conditions. A new set of compounds was prepared for this study and their XANES spectra are analyzed for comparison with potential extraterrestrial analogs. S K -edge XANES spectra were collected at Argonne National Lab for FeS (troilite), MnS (alabandite), CaS (oldhamite), MgS (niningerite), Ni 1–x S, NiS 2 , CaSO 4 (anhydrite), MgSO 4 , FeSO 4 , Fe 2 (SO 4 ) 3 , FeCr 2 S 4 (daubréelite), Na 2 S, Al 2 S 3 , Ni 7 S 6 , and Ni 3 S 2 ; the latter five were analyzed for the first time using XANES. These standards expand upon the existing S XANES end-member libraries at a higher spectral resolution (0.25 eV steps) near the S K -edge. Processed spectra, those that have been normalized and “flattened,” are compared to quantify uncertainties due to data processing methods. Future investigations that require well-characterized sulfide standards, such as the ones presented here, may have important implications for understanding sulfur speciation in reduced silicate glasses and minerals with applications for the early Earth, Moon, Mercury, and enstatite chondrites.

Anzures, Brendan A.↗
DOE OSTI2026

Studying MeV Scale Neutron Interactions in DUNE ND-LAr 2x2 Demonstrator

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino oscillation experiment designed to make high-precision measurements of neutrino oscillation parameters and probe for new physics using a neutrino beam produced at Fermilab and measured at a near detector complex and a far detector located 1,300 km away at the Sanford Underground Research Facility. Precise neutrino energy reconstruction is essential for these measurements, with neutron production in neutrino–argon interactions representing a significant source of systematic uncertainty, as neutrons can carry away significant energy, making their detection and characterization crucial. DUNE’s near detector complex includes ND-LAr, a Liquid Argon Time Projection Chamber (LArTPC) detector designed to mirror the far detector technology and constrain neutrino–argon interaction systematics. The DUNE 2x2 Demonstrator, a pixelated LArTPC based on the ArgonCube design, serves as a prototype for ND-LAr. During 2x2 operations from October to November 2026, an Americium–Beryllium (AmBe) neutron source was deployed near the detector cryostat to obtain a high-statistics sample of neutron interactions. This poster presents progress in studying MeV-scale neutron interactions in this dataset by identifying de-excitation gammas from neutron capture on argon, providing a method for neutron identification and charge readout system calibration in future DUNE detectors.

Mao, Edgar [Syracuse U.] (ORCID:0009000600893306)↗
DOE OSTI2022

Measurement Uncertainty of Highly Asymmetrically Curved Elliptical Mirrors Using Multi-Pitch Slope Stitching Technique

Soft X-ray off-axis elliptical mirrors bring new challenges for X-ray mirror metrology. These highly asymmetrically curved elliptical cylindrical mirrors with a total slope range >10 mrad are extremely challenging to measure. Their total slope range exceeds the measuring range of most angular sensors used for X-ray mirror inspection. To overcome this problem, it is possible to stitch partial slope data by measuring the mirror at different pitch angles (multi-pitch angles). By revisiting the theory of the multi-pitch Nano-accuracy Surface Profiler (NSP), we derive the sampling position error on the mirror surface as a function of the mirror height profile and the measurement error of the pitch rotation center. When measuring “extreme”, highly asymmetrically curved, elliptical mirrors, the calculation of the mirror height profile with iterative reconstruction outperforms the classical “flat assumption” (i.e., assuming that the mirror sag is negligible). As demonstrated by our simulations, a proper tolerance evaluation on the measurement of pitch rotation center is needed to assess the measurement accuracy (systematic error) for these strongly aspherical mirrors using the multi-pitch NSP technique. Taking a real design of an “extreme” elliptical mirror as a case study, we conduct a Monte Carlo simulation to mimic the measurement and characterization process to analyze the impact of several error sources. With the measurement uncertainty of the pitch rotation center, the multi-pitch NSP measurement can estimate the grazing angle θ and the chief ray location x o with their uncertainties, as well as the slope residuals.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Generating Uncertainty Distributions for Seismic Signal Onset Times

Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

58 GEOSCIENCES↗
DOE OSTI2021

Performance Comparison of Circular and Spherical Error Probable Estimators

This report compares the performance of three Circular Error Probable (CEP) estimators: the Grubbs-Patnaik estimator, a new, non-iterative, radial-integration estimator, and a median estimator. It also compares the performance of two Spherical Error Probable (SEP) estimators. The performance of each estimator is assessed in terms of bias, uncertainty, robustness, and computational complexity. Robustness is evaluated with respect to outliers, variations in the underlying statistical distribution characterizing munition impact positions, and impact-position measurement errors. The performance assessments indicate the radial-integration and Grubbs-Patnaik estimators perform nearly identically providing the statistical distribution of impact-position coordinates is jointly normal with zero means. In that case, both estimators outperform the median estimator by about 2% relative to the true CEP in terms of estimator uncertainty. The bias performance of the radial-integration and median estimators is close to zero for jointly normal impacts, however, the Grubbs-Patnaik estimator can be significantly biased for jointly normal impacts with non-zero means. When the statistical distribution characterizing impact positions is known, but not jointly normal, the radial-integration estimator is superior. In this case, the median estimator also outperforms the Grubbs-Patnaik estimator but is not quite as good as the radial-integration estimator. If the statistical distribution characterizing impacts is unknown and not jointly normal, or if distribution parameters are difficult or impractical to estimate, or if test data is corrupted with outliers, then the median estimator dramatically outperforms the other estimators, especially in terms of estimation bias. Unexpectedly, measurement noise did not significantly degrade the performance of any of the estimators, except for cases with signal to noise ratios less than five. Although the Grubbs-Patnaik estimator has remained the gold standard for CEP estimation for over half a century, the performance assessments indicate the new, non-iterative, radial-integration estimator and the median estimator offer significant advantages and, in most practical real-world conditions, are superior estimators. These estimators are also useful for SEP estimation whereas the Grubbs-Patnaik estimator does not extend to three dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Systems Theory Principles and Complex Systems Engineering Concepts for Protection and Resilience in Critical Infrastructure: Lessons from the Nuclear Sector

Part of the Presidential Policy Directive 21 (PPD-21) (PPD 2013) mandate includes evaluating safety, security, and safeguards (or nonproliferation) mechanisms traditionally implemented within the nuclear reactors, materials, and waste sector of critical infrastructure—including a complex, dynamic set of risks and threats within an all-hazards approach. In response, research out of Sandia National Laboratories (Sandia) explores the ability of systems theory principles (hierarchy and emergence) and complex systems engineering concepts (multidomain interdependence) to better understand and address these risks and threats. Herein, this Sandia research explores the safety, safeguards, and security risks of three different nuclear sector-related activities—spent nuclear fuel transportation, small modular reactors, and portable nuclear power reactors—to investigate the complex and dynamic risk related to the PPD-21-mandated all-hazards approach. This research showed that a systems-theoretic approach can better identify inter-dependencies, conflicts, gaps, and leverage points across traditional safety, security, and safeguards hazard mitigation strategies in the nuclear reactors, materials, and waste sector. Resulting from this, mitigation strategies from applying systems theoretic principles and complex systems engineering concepts can be (1) designed to better capture interdependencies, (2) implemented to better align with real-world operational uncertainties, and (3) evaluated as a systems-level whole to better identify, characterize, and manage PPD-21's all hazards strategies.

42 ENGINEERING↗
DOE OSTI2024

Automated Gold Nanorod Spectral Morphology Analysis Pipeline

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

Abstract Global climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Neutron Detection in the DUNE liquid argon near Detector

DUNE is a next-generation long baseline neutrino experiment [1] to perform precision measurements of neutrino oscillations. The Liquid Argon Time Projection Chamber (LArTPC) of the DUNE near detector (ND-LAr) characterizes the beam before oscillations occur and is essential to constrain the systematic uncertainties of most DUNE measurements. ND-LAr will be a modular LArTPC with pixelated charge and high-coverage light readout system

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

pH dependence of brown-carbon optical properties in cloud water

Abstract. Light-absorbing organic species present in aerosols, collectively called brown carbon (BrC), have important but highly uncertain effects on climate. Clouds likely represent a significant medium for secondary BrC production and for bleaching reactions, though the relative importance of the formation and loss processes in clouds is unknown at present. The acidity (or pH) of atmospheric particles and clouds affects the optical properties of BrC and bleaching rates. Given the wide variability of pH in the atmosphere (pH in particles and clouds ranges from −1 to 8), the optical properties of BrC and its bleaching behavior are expected to vary significantly, and the link between pH and BrC is yet another uncertainty in attempts to constrain its climate forcing effects. In this work, we characterize the pH dependence of BrC optical properties – including light absorption at 365 nm (Abs365), the mass absorption coefficient (MAC365), and the absorption Ångström exponent (AAE) – in bulk cloud water sampled from the summit of Whiteface Mountain, NY. In all samples (n=17), Abs365 and MAC365 increased linearly with increasing pH, highlighting the importance of reporting pH in studies of BrC in aqueous media. There was strong variability in the sensitivity of Abs365 to pH, with normalized slopes that ranged from 5.1 % to 17.2 % per pH unit. The normalized slope decreased strongly with increasing cloud water [K+], suggesting that the non-biomass-burning BrC has optical properties that are more sensitive to pH than BrC associated with biomass burning. AAE also showed a distinct pH dependence as it was relatively flat between pH 1.5–5 and then decreased significantly above pH 5. The cloud water composition was used to inform thermodynamic predictions of aerosol pH upwind and/or downwind of Whiteface Mountain and the subsequent changes in BrC optical properties. Overall, these results show that, in addition to secondary BrC production, photobleaching, and the altitudinal distribution, the climate forcing of BrC is quite strongly affected by its pH-dependent absorption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimating Uncertainty in Simulated ENSO Statistics

Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

54 ENVIRONMENTAL SCIENCES↗