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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

A multi-model CMIP6-PMIP4 study of Arctic sea ice at 127 ka: sea ice data compilation and model differences

The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×10^6 sq.km, compared to 6.46×10^6 sq.km for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×10^6 sq.km for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 %/yr) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Arctic sea ice↗
NASA NTRSDate not supplied

An Ensemble Investigation of the Causes for Regional Air-Quality Model Critical Load Exceedances Prediction Variability in European and North American Domains Using Diagnostics From Phase 4 of the Air Quality Model Evaluation International Initiative

We summarize tentative findings from multi air quality model ensembles for the years 2009 and 2010 in Europe (EU), and 2010 and 2016 in North America (NA), under AQMEII-4. The model predictions of sulphur and nitrogen deposition were used to estimate exceedances of critical loads for acidification and eutrophication, to show the extent to which the ensemble members agree in the magnitude and the trend of ecologically meaningful impacts. Model exceedance variability was analyzed using AQMEII-4 diagnostics. Evaluation against concentration and wet deposition observations, coupled with these diagnostics, identified specific process representations as the causes for variability between model predictions and for reduced model performance. All models predicted reductions in ecosystem acidification impacts in North America between the years 2010 and 2016, in accord with SO2 emissions reduction legislation which started in 2010 (SO2 SIP) However, all models in EU and NA domains had net negative biases for wet deposition of sulphur and nitrogen relative to observations. The wet S deposition average mean bias for the NA ensemble was -0.17 eq ha-1 d-1, and for the EU ensemble -1.15 eq ha-1 d-1. The NA daily wet deposition average mean bias for NH4+ was -0.37 eq ha-1d-1; EU -1.19 eq ha-1 d-1. The daily NA wet NO3- deposition average mean bias was -0.24 eq ha-1d-1; EU -0.69 eq ha-1 d-1. The members of the ensemble diverged (factor of 10) in their North American predictions for Ndep and consequently their eutrophication exceedances. The models with the highest eutrophication predictions also predicted the highest levels of gas-phase ammonia dry deposition (standard deviation of ammonia dry deposition flux across ensemble members was larger than the ensemble average). These models also had negative biases of predicted ammonia concentrations; average mean biases of -0.63 (satellite NH3) and -0.85 ppbv (surface NH3) compared to ensemble averages of -0.30 and -0.34 ppbv. Diagnostics showed that these differences resulted from the manner in which bidirectional ammonia fluxes were parameterized within these models. The second largest source of NA eutrophication prediction variability were models with positive biases in particulate ammonium and nitrate concentrations, and higher particle nitrogen deposition levels ( particle ammonium concentration bias +0.35 ug m-3; ensemble bias +0.15 ug m-3). We believe two factors may have led to these latter overestimates: higher levels of fine mode particle nitrate formation compared to other models (due to the use of an inorganic heterogeneous chemistry algorithm which did not take base cation chemistry into account), and updates to particle dry deposition velocities carried out in the absence of concurrent updates to wet scavenging algorithms. The relative importance of dry gas, dry particulate, and wet deposition towards total sulphur and nitrogen deposition totals differed between EU and North American domains, though all models had negative biases in wet deposition as noted above. Parallel and subsequent work suggests that multiphase hydrometeor scavenging may improve model wet deposition performance. An increased research focus is recommended for four model processes: multiphase hydrometeor scavenging, ammonia bidirectional fluxes, base cation chemistry and emissions, and particle dry deposition.

regional air-quality model↗
DOE OSTI2017

Synthesis and Electrocatalytic Activity of [FeFe]‐Hydrogenase Model Complexes with Non‐Innocent Chelating Nitrogen‐Donor Ligands

To probe the influence of redox non‐innocent ligands on a well‐known class of [FeFe]‐hydrogenase models, three new asymmetrically disubstituted diiron complexes of the general formula (µ‐SRS)[Fe(CO) 3 ][Fe(CO)(N‐N)] {SRS = propane‐1,3‐dithiolate (pdt) or benzene‐1,2‐dithiolate (bdt), and N‐N = 2,2′‐bipyridine (bipy) or 2,2′‐bipyrimidine (bpym)} have been synthesized from their parent hexacarbonyls and characterized. The new complexes, (µ‐pdt)Fe 2 (CO) 4 (κ 2 ‐bpym) ( 2 ), (µ‐bdt)Fe 2 (CO) 4 (κ 2 ‐bipy) ( 3 ), and (µ‐bdt)Fe 2 (CO) 4 (κ 2 ‐bpym) ( 4 ), were fully characterized by spectroscopic and electrochemical techniques, and the results are compared to those of a related complex, (µ‐pdt)Fe 2 (CO) 4 (κ 2 ‐bipy) ( 1 ). The crystal structures of 2 – 4 show that, in each complex, the two iron units are in an eclipsed orientation, and the N‐N ligand lies in the basal plane. IR spectra and electrochemical analyses indicate that electron density at the iron centers decreases in the order 1 > 2 > 3 > 4 . Furthermore, compound 2 undergoes a ligand‐centered reduction at the same potential that the hexacarbonyl precursor undergoes its first reduction. However, unlike the 2,2′‐bipy derivatives 1 and 3 , the 2,2′‐bpym complexes 2 and 4 are not effective catalysts for electrochemical proton reduction from acetic acid.

Roy, Souvik↗
DOE OSTI2014

Electrochemical solvent reorganization energies in the framework of the polarizable continuum model

Electron transfer reactions at electrochemical interfaces play a critical role in a wide range of catalytic processes. A key parameter in the rate constant expressions for such processes is the reorganization energy, which reflects the energetic cost of the solute and solvent rearrangements upon electron transfer. In this paper, we present dielectric continuum methods for calculating the solvent reorganization energy for electrochemical processes. We extend the simple approach in which the solute is represented as a point charge located a specified distance from the electrode surface to the representation of the solute as a collection of point charges corresponding to the partial atomic charges of the molecule. We also develop a method for calculating the electrochemical solvent reorganization energies with molecular-shaped cavities within the framework of the polarizable continuum model (PCM). The electronic and inertial responses of the solvent are separated according to their respective time scales, and two limiting cases of the relation between the solute and solvent electrons are examined. The effects of the electrode are included with the integral equations formalism PCM (IEF-PCM), in which the molecule-solvent boundary is treated explicitly, but the effects of the electrode-solvent boundary are included through an external Green’s function. This approach accounts for the effects of detailed molecular charge redistribution in a molecular-shaped cavity, as well as the electronic and inertial solvent responses and the effects of the electrode. The calculated total reorganization energies are in reasonable agreement with experimental measurements for a series of electrochemical systems. Inclusion of the effects of the electrode is found to be essential for obtaining even qualitatively accurate solvent reorganization energies. These approaches are applicable to a wide range of systems and can be extended to include other types of boundaries, such as a self-assembled monolayer or double layer separating the electrode and the molecule. This research was supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences.

Ghosh, Soumya↗
DOE OSTI2014

Evolution of helical perturbations in a thin-shell model of an imploding liner

A thin-shell model of the liner stability has been revisited and applied to the stability of the helical perturbations. Several stages of the implosion have been identified, starting from a long initial “latent” phase of an almost resting liner, continuing to the second stage of a rapid contraction and significant perturbation growth, and then transitioning to the third stage where perturbations become ballistic and highly non-linear. The stage of stagnation and rebound is beyond the scope of this paper. An importance of vorticity conservation during the late stages is emphasized. Nonlinear evolution of perturbations is followed up to the point of the formation of cusp structures. Effects of in-surface flows and of their enhancement due to the vorticity conservation are discussed. It is shown that the pre-machined perturbations created only on the outer surface of the liner grow much slower than one could anticipate. The limitations on the thin-shell description are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Modeling the one-dimensional imager of neutrons (ODIN) for neutron response functions at the Sandia Z facility

The one-dimensional imager of neutrons (ODIN) at the Sandia Z facility consists of a 10-cm block of tungsten with rolled edges, creating a slit imager with slit widths of either 250, 500, or 750 μm. Designed with a 1-m neutron imaging line of sight, we achieve about 4:1 magnification and 500-μm axial spatial resolution. The baseline inertial confinement fusion concept at Sandia is magnetized liner inertial fusion, which nominally creates a 1-cm line source of neutrons. ODIN was designed to determine the size, shape, and location of the neutron producing region, furthering the understanding of compression quality along the cylindrical axis of magnetized liner implosions. Challenges include discriminating neutrons from hard x-rays and gammas with adequate signal-to-noise in the 2 × 10 12 deuterium-deuterium (DD) neutron yield range, as well as understanding the point spread function of the imager to various imaging detectors (namely, CR-39). Modeling efforts were conducted with MCNP6.1 to determine neutron response functions for varying configurations in a clean DD neutron environment (without x-rays or gammas). Configuration alterations that will be shown include rolled-edge slit orientation and slit width, affecting the resolution and response function. The experiment to determine CR-39 neutron sensitivity, with and without a high density polyethylene (n, p) converter, an edge spread function, and resolution will be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Vehicle Modeling for use in the CAFE model: Process description and modeling assumptions

The objective of this project is to develop and demonstrate a process that, at a minimum, provides more robust information that can be used to calibrate inputs applicable under the CAFE model’s existing structure. The project will be more fully successful if a process can be developed that minimizes the need for decision trees and replaces the synergy factors by inputs provided directly from a vehicle simulation tool. The report provides a description of the process that was developed by Argonne National Laboratory and implemented in Autonomie.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1975

Results from investigations in the NASA/MSFC TWT on a .004 scale model space shuttle launch vehicle (model 13P-OTS) to determine gas supply strut effects on model pressure environment (IA53)

The Rockwell Space Shuttle Launch Vehicle components were tested in a Trisonic Wind Tunnel. Pressure measurements were made on the aft portion, in the base regions, and on the wing surfaces. Axial force determination were made from the pressure data. Data were recorded with the model at zero degrees angle of attack and sideslip through a Mach number from 0.9 to 3.0. The only configuration changes investigated were strut mounting techniques and gas line fairing location between the external tank and the orbiter.

Garton, W. P.↗
NASA NTRS1983

Modelling atmospheric aerosol backscatter at CO2 laser wavelengths. I - Aerosol properties, modeling techniques, and associated problems. II - Modeled values in the atmosphere. III - Effects of changes in wavelength and ambient conditions

The various methods of calculating the atmospheric aerosol backscattering function, beta(CO2), both from measured aerosol characteristics and from optical measurements made at other wavelengths, are discussed in detail, with limits placed on their accuracy. The most significant factor in determining beta(CO2) is found to be the aerosol size distribution and concentration; this should be known accurately for particle radii up to at least 1 micron for stratospheric particles and 5 microns for tropospheric particles. Results are then presented from the modeling of the aerosol backscattering function at a wavelength of 10.6 microns in the lowest 20 km of the atmosphere. It is found that beta(CO2) varies from 10 to the -6th per m per sr in the planetary boundary layer to less than 10 to the -11th per m per sr in the stratosphere. It is next shown that, with the exception of (NH4)2SO4-containing aerosols, whose size distributions have relatively large numbers of small particles, the variation of backscattering with CO2 wavelength is less than a factor of approximately 3. For such (NH4)2SO4 aerosol distributions, however, the variation of backscatter function with CO2 wavelengths between 9.1 and 11.1 microns may reach one order of magnitude.

Kent, G.S.↗
NASA NTRS1992

A model of nitrous oxide evolution from soil driven by rainfall events. I - Model structure and sensitivity. II - Model applications

Simulations of N2O and CO2 emissions from soils were conducted with a rain-event driven, process-oriented model (DNDC) of nitrogen and carbon cycling processes in soils. The magnitude and trends of simulated N2O (or N2O + N2) and CO2 emissions were consistent with the results obtained in field experiments. The successful simulation of these emissions from the range of soil types examined demonstrates that the DNDC will be a useful tool for the study of linkages among climate, soil-atmosphere interactions, land use, and trace gas fluxes.

Changsheng, LI↗
NASA NTRS2002

Technical Report Series on Global Modeling and Data Assimilation: A Coupled Ocean-Atmosphere Radiative Model for Global Ocean Biogeochemical Models - Volume 22

An ocean-atmosphere radiative model (OARM) evaluates irradiance availability and quality in the water column to support phytoplankton growth and drive ocean thermodynamics. An atmospheric component incorporates spectral and directional effects of clear and cloudy skies as a function of atmospheric optical constituents, and spectral reflectance across the air-sea interface. An oceanic component evaluates the propagation of spectral and directional irradiance through the water column as a function of water, five phytoplankton groups, and chromophoric dissolved organic matter. It tracks the direct and diffuse streams from the atmospheric component, and a third stream, upwelling diffuse irradiance. The atmospheric component of OARM was compared to data sources at the ocean surface with a coefficient of determination (r2) of 0.97 and a root mean square of 12.1%.

Gregg, Watson W.↗
DOE OSTI2022

Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models

Three model configurations are presented for multi-step time series predictions of the heat absorbed by the water and steam in a thermal power plant. The models predict over horizons of 2, 4, and 6 steps into the future, where each step is a 5-minute increment. The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset. The hybrid model deconstructs the machine learning into individual boiler heat absorption units: economizer, water wall, superheater, and reheater. Each configuration uses a gated recurrent unit (GRU) or a GRU-based encoder–decoder as the deep learning architecture. Mean squared error is used to evaluate the models compared to target values. The encoder–decoder architecture is over 11% more accurate than the GRU only models. The hybrid model with the incomplete dataset highlights the importance of the manipulated variables to the system. The hybrid model, compared to the pure machine learning model, is over 10% more accurate on average over 20 iterations of each model. Automatic differentiation is applied to the hybrid model to perform a local sensitivity analysis to identify the most impactful of the 72 manipulated variables on the heat absorbed in the boiler. The models and sensitivity analyses are used in a discussion about optimizing the thermal power plant.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models

For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. Here, we propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A new multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential processes under process model and parametric uncertainty

Process-based models have been widely used for hydrologic modeling, and it is a common practice to use sensitivity analysis methods for excluding non-influential hydrologic processes from further investigation and/or model improvement. This study develops a new method called multi-model absolute difference-based sensitivity (MMADS) analysis method to screen non-influential system processes and parameters. MMADS is conceptually similar to the Morris method for addressing parametric uncertainty, but has a unique feature to address both process model uncertainty (i.e., a process may be represented by multiple process models) and process model parameter uncertainty (i.e., parameters associated with a process model are random). MMADS first evaluates absolute differences of a quantity of interest (i.e., a system model output) by varying process models and/or process model parameter values, and then calculates the mean and variance of the differences for investigating process influence. The mean measures overall influence of the process on the quantity of interest, and the variance estimates influence of nonlinear effects of the process and/or its interactions with other processes. MMADS is an extension of the Morris method from a parameter space to a joint parameter-model space for explicitly addressing both process model uncertainty and model parameter uncertainty. The performance of MMADS is evaluated by using two numerical experiments. One experiment is based on Sobol’s G*-function with ten product elements, and has analytical solutions of the MMADS mean and variance of absolute differences. The other experiment is for groundwater flow modeling which considers three processes (i.e., recharge, geology, and snowmelt) that interact with each other. Finally, results indicate that MMADS is computationally efficient and can identify non-influential processes of complex hydrological systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Analyzing the impact of modeling choices and assumptions in compartmental epidemiological models

In this study, computational models have become increasingly used as part of modeling, predicting, and understanding how infectious diseases spread within large populations. These models can be broadly classified into differential equation-based models (EBM) and agent-based models (ABM). Both types of models are central in aiding public health officials design intervention strategies in case of large epidemic outbreaks. We examine these models in the context of illuminating their hidden assumptions and the impact these may have on the model outcomes. Very few ABM/EBMs are evaluated for their suitability to address a particular public health concern, and drawing relevant conclusions about their suitability requires reliable and relevant information regarding the different modeling strategies and associated assumptions. Hence, there is a need to determine how the different modeling strategies, choices of various parameters, and the resolution of information for EBMs and ABMs affect outcomes, including predictions of disease spread. In this study, we present a quantitative analysis of how the selection of model types (i.e., EBM vs. ABM), the underlying assumptions that are enforced by model types to model the disease propagation process, and the choice of time advance (continuous vs. discrete) affect the overall outcomes of modeling disease spread. Our study reveals that the magnitude and velocity of the simulated epidemic depends critically on the selection of modeling principles, various assumptions of disease process, and the choice of time advance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Technical Note: On the use of nudging for aerosol–climate model intercomparison studies

Nudging as an assimilation technique has seen increased use in recent years in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5 (CAM5), due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on long-wave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations, and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. Results from both CAM5 and a second aerosol–climate model ECHAM6-HAM2 also indicate that compared to the wind-and-temperature nudging, constraining only winds leads to better agreement with the free-running model in terms of the estimated shortwave cloud forcing and the simulated convective activities. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects since it provides well-constrained meteorology without strongly perturbing the model's mean climate.

Zhang, K.↗