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DOE OSTI2012

Analyses Of Two End-User Software Vulnerability Exposure Metrics

The risk due to software vulnerabilities will not be completely resolved in the near future. Instead, putting reliable vulnerability measures into the hands of end-users so that informed decisions can be made regarding the relative security exposure incurred by choosing one software package over another is of importance. To that end, we propose two new security metrics, average active vulnerabilities (AAV) and vulnerability free days (VFD). These metrics capture both the speed with which new vulnerabilities are reported to vendors and the rate at which software vendors fix them. We then examine how the metrics are computed using currently available datasets and demonstrate their estimation in a simulation experiment using four different browsers as a case study. Finally, we discuss how the metrics may be used by the various stakeholders of software and to software usage decisions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Using Big Data Technologies for HEP Analysis

The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother. In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis. The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow.

Cremonesi, Matteo↗

DOE OSTI2020

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appropriate regularization term. Two standard regularization terms are the L1 norm and the total variation (TV) norm. We present a comparison of CS reconstruction results based on these two approaches in the context of chemical detection, and we demonstrate that optimization based on the L1 norm outperforms optimization based on the TV norm. Our comparison is driven by CS sampling, reconstruction, and chemical detection in two real-world datasets: the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Both datasets contain the release of a chemical simulant such as glacial acetic acid, triethyl phosphate, and sulfur hexafluoride. For chemical detection we use the adaptive coherence estimator (ACE) and bulk coherence, and we propose algorithmic ACE thresholds to define the presence or absence of a chemical of interest in both un-compressed data cubes and reconstructed data cubes. The un-compressed data cubes provide an approximate ground truth. We demonstrate that optimization based on either the L1 norm or TV norm results in successful chemical detection at a compression rate of 90%, but we show that L1 optimization is preferable. We present quantitative comparisons of chemical detection on reconstructions from the two methods, with an emphasis on the number of pixels with an ACE value above the threshold.

Hyperspectral imagery, L1-norm, total variation, A↗

DOE OSTI2013

A SYSTEMATIC ANALYSIS OF CAUSTIC METHODS FOR GALAXY CLUSTER MASSES

We quantify the expected observed statistical and systematic uncertainties of the escape velocity as a measure of the gravitational potential and total mass of galaxy clusters. We focus our attention on low redshift (z {<=}0.15) clusters, where large and deep spectroscopic datasets currently exist. Utilizing a suite of Millennium Simulation semi-analytic galaxy catalogs, we find that the dynamical mass, as traced by either the virial relation or the escape velocity, is robust to variations in how dynamical friction is applied to ''orphan'' galaxies in the mock catalogs (i.e., those galaxies whose dark matter halos have fallen below the resolution limit). We find that the caustic technique recovers the known halo masses (M{sub 200}) with a third less scatter compared to the virial masses. The bias we measure increases quickly as the number of galaxies used decreases. For N{sub gal} > 25, the scatter in the escape velocity mass is dominated by projections along the line-of-sight. Algorithmic uncertainties from the determination of the projected escape velocity profile are negligible. We quantify how target selection based on magnitude, color, and projected radial separation can induce small additional biases into the escape velocity masses. Using N{sub gal} = 150 (25), the caustic technique has a per cluster scatter in ln (M|M{sub 200}) of 0.3 (0.5) and bias 1% {+-} 3{r_brace} (16% {+-} 5{r_brace}) for clusters with masses >10{sup 14} M{sub Sun} at z < 0.15.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A seamless approach for evaluating climate models across spatial scales

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Whose Gas is it anyway? Differentiating the Source of a Large Soil Vapor Plume beneath Two Adjacent Waste Sites - 20487

DOE contractor CH2M Hill Plateau Remediation Company is currently responsible for conducting groundwater contamination monitoring at several RCRA treatment, storage, and disposal units located on the Hanford Site in Richland, Washington State. The Nonradioactive Dangerous Waste Landfill treatment, storage, and disposal unit presents a distinct groundwater monitoring problem because of a large multi-contaminant soil vapor plume beneath it that is a likely source of low-level volatile organic compound groundwater contamination. Adjacent to Nonradioactive Dangerous Waste Landfill is the Solid Waste Landfill. Volatile organic compounds are inventory components of both the Nonradioactive Dangerous Waste Landfill and the Solid Waste Landfill. Therefore, it is possible that both sites could be contributing to the soil vapor plume. For regulatory purposes, it is important to differentiate which site is the primary contributor of volatile organic compounds to the plume. An approach was developed to identify the primary volatile organic compound source of the soil vapor plume beneath Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. The site conceptual model hypothesis of vapor-phase volatile organic compound transport to the dissolved phase in groundwater was tested by a simple mathematical model of vapor/liquid equilibrium concentrations at the groundwater/air interface. Once it was shown that vapor-phase volatile organic compound transport to groundwater was a valid conceptual model for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill, spatial and statistical methods were used to determine the primary site contributing to the majority of volatile organic compounds to the soil vapor plume. Average groundwater chloroform, tetrachloroethene, and trichloroethene concentrations from Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring network wells were plotted on maps of the facilities and immediate vicinities and compared to soil vapor sampling probe locations. Principal component analysis and mixing ratios were used to identify source contributions of each treatment, storage, and disposal unit to the plume. Results of the vapor/liquid equilibrium concentrations mathematical model showed that transport phenomena outweigh steady-state equilibria. Estimated vapor/liquid equilibrium concentrations were considerably lower than soil vapor measurements. The results indicate that dynamic vadose zone and groundwater factors such as decreased vapor concentrations with depth, vapor dilution from dispersion in the vadose zone, and advective and diffusional volatile organic compound dilution in groundwater result in groundwater volatile organic compound concentrations much less than would be measured under steady-state equilibrium conditions. Site source contribution differentiation by principal component analysis and mixing ratios was inconclusive using actual soil gas data because of the similarity in concentration values in both datasets for Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill. Similar data populations suggest mixing of the vapor contributions from both sites by dispersion through the soil matrix pore spaces. However, when groundwater volatile organic compound data were compared between the Nonradioactive Dangerous Waste Landfill and Solid Waste Landfill monitoring networks, Solid Waste Landfill mean concentrations were higher, suggesting more vapor-phase volatile organic compound transport to groundwater at those locations. Simulated volatile organic compound soil vapor and groundwater datasets created to test the methods developed for this study show that the method can be successful in source differentiation when significantly different datasets are compared. This paper will describe a method of testing a conceptual model for vapor-phase contaminant transport to groundwater and for differentiating site sources of contaminants comprising a mixed-constituent soil vapor plume. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1995

Mine Discrimination Using Multispectral Imagery With Feedforward Neural Networks

Simulated mine detection was performed on a polarimetric hyperspectral imaging dataset collected by using an acousto-optic tunable filter camera. A feedforward artificial neural network was programmed to recognize predefined spectral "templates." The simulation results are provided along with the preprocessing steps and window sizes leading to mine detection without false alarms.

polarimetric↗

DOE OSTI2022

High-Fidelity Simulation Study of the Unsteady Flow Effects on High-Pressure Turbine Blade Performance

Abstract Unsteadiness, in the form of both broadband background disturbances and discrete coherent wakes, can have a strong effect on the performance of turbomachinery blades. The influence of the incoming flow has received much interest as it inevitably affects the blade boundary layers and develops as it passes through the machine. In the present work, we investigate the effect of unsteady flow on high-pressure turbines (HPTs), using high-fidelity datasets produced by wall resolved large-eddy simulation of an HPT stage. The effects of incident wakes from an upstream stator, compounded by the presence of freestream turbulence, on the downstream rotor are investigated. Based on analyzing cases with different turbulence intensities and length scales prescribed at the inlet, we show that changing the freestream turbulence characteristics has a direct effect on the unsteady behavior of the stator wakes. As a result, the performance of the rotor is also significantly affected. By detailing the influence of the wake–turbulence interaction, we aim to distinguish driving forces on rotor performance, be it changes in the incident wakes or direct influence from the freestream turbulence. Furthermore, the aerothermal behaviors of the rotor blades have been extensively investigated, showing that the blade boundary layers on the suction and pressure sides respond differently to external disturbances. The insights gained can provide designers with guidelines in understanding the unsteady flow effects of a given flow state, and how the unsteadiness present, either broadband or deterministic, will affect the performance of downstream blades.

Engineering↗

NASA NTRS2019

Evaluating the Uncertainty of Terrestrial Water Budget Components over High Mountain Asia

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ± 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

Terrestrial water budget↗

DOE OSTI2023

Using deep machine learning to interpret proton radiography data from a pulsed power experiment

Deep machine learning is used to analyze a proton radiograph from a tin pulsed power experiment and determine density values for each pixel in the image. Two promising convolutional neural network architectures that have proven to be effective for image analysis in other applications are applied to analyze a proton radiograph and find density values. The process of creating a suitable training dataset is described, involving the Lagrangian hydrodynamic model used for simulations of the experiment, the proton radiography forward model to make synthetic images for training, and the manner in which data augmentation is used to expand the resulting image dataset. It is shown that machine learning not only produces a reasonable density field but is also able to predict features in the density field that are suggested by the proton radiograph but not captured by simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Leaf-scale carbon assimilation simulated by the multi-assumption architecture & testbed for process sensitivity analysis

Raw and processed output from the Multi-Assumption Architecture & Testbed (MAAT, https://github.com/walkeranthonyp/MAAT, tag: v1.2.1_walker2020; Walker et al., 2018). The dataset contains leaf-scale carbon assimilation data from three MAAT simulations and their post-processing and plotting scripts. Simulation 1, labelled "FvC_4proc_proc," is a four-process, process sensitivity analysis (Dai et al., 2017) of carbon assimilation based on the Farquhar et al. (1980) photosynthesis model, and its variant proposed by Collatz et al. (1991). Simulation 2, labelled "FvC_4proc_salt," is a 14 parameter sensitivity analysis (Saltelli et al., 2010) of carbon assimilation based on the Farquhar and Collatz photosynthesis models. Simulation 3, labelled "FvC," is a factorial combination of the alternative process representations and parameter values presented in those original papers across a factorial combination of incident photosynthetically active radiation (50-2000 µmol m-2 s-1, in increments of 50) and atmospheric CO2 concentration (50-1500 µmol m-2 s-1, in increments of 50). Additional scripts/functions for post-processing and plotting are part of the MAAT repository. These data were generated to evaluate and compare the Farquhar and Collatz models of leaf-scale photosynthesis across a range of CO2 and incident light conditions. Analysis of results can be found in Walker et al. (2020). This dataset is comprised of R scripts to process data and produce figures; .RDS files, a binary format specific to R and read with the 'readRDS' function, to store raw MAAT output; R and bash scripts (.bs) that help to run a MAAT simulation, .XML files that are output from MAAT describing a run, and .csv files which contain the calculated sensitivity indexes published in Walker et al. (2020).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Evaluation of Global Monsoon Precipitation Changes based on Five Reanalysis Datasets

With the motivation to identify whether or not a reasonably simulated atmospheric circulation would necessarily lead to a successful reproduction of monsoon precipitation, the performances of five sets of reanalysis data (NCEP2, ERA40, JRA25, ERA-Interim and MERRA) in reproducing the climatology, interannual variation and long-term trend of global monsoon (GM) precipitation are comprehensively evaluated. In order to better understand the variability and long-term trend of GM precipitation, we also examined the major components of water budget, including evaporation, water vapor convergence and the change in local water vapor storage, based on five reanalysis datasets. The results show that all five reanalysis data reasonably reproduce the climatology of GM precipitation. The ERA-Interim (NCEP2) shows the highest (lowest) skill among the five datasets. The observed GM precipitation shows an increasing tendency during 1979-2001 along with a strong interannual variability, which is reasonably reproduced by the five sets of reanalysis data. The observed increasing trend of GM precipitation is dominated by the contribution from the North African, North American and Australian monsoons. All five data fail in reproducing the increasing tendency of North African monsoon precipitation. The wind convergence term in water budget equation dominate the GM precipitation variation, indicating a consistency between the GM precipitation and the seasonal change of prevailing wind.

Climate↗

DOE OSTI2016

MULTILEVEL MONTE CARLO (MLMC) SIMULATIONS: PERFORMANCE RESULTS FOR SPE10 (XY SLICES)

In this report we first describe a generic multilevel Monte Carlo method and then illustrate its superior performance over a traditional single-level Monte Carlo method for second order elliptic PDEs corresponding to two-dimensional layers in (x, y)-direction of the Tenth SPE Comparative Solution project (SPE 10) which gives high-contrast permeability coefficients. The SPE10 data set is used as a coarse level in the Monte Carlo method and the respective permeability coefficient k (provided in the SPE10 dataset) is used as a mean in the simulation. The actual coefficients are drawn based on a KL-expansion assuming that the log-mean is perturbed by a log-normal distributed samples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Study of energy conversion and partitioning in the magnetic reconnection layer of a laboratory plasma

While the most important feature of magnetic reconnection is that it energizes plasma particles by converting magnetic energy to particle energy, the exact mechanisms by which this happens are yet to be determined despite a long history of reconnection research. Recently, we have reported our results on the energy conversion and partitioning in a laboratory reconnection layer in a short communication [Yamada et al., Nat. Commun. 5, 4474 (2014)]. The present paper is a detailed elaboration of this report together with an additional dataset with different boundary sizes. Our experimental study of the reconnection layer is carried out in the two-fluid physics regime where ions and electrons move quite differently. We have observed that the conversion of magnetic energy occurs across a region significantly larger than the narrow electron diffusion region. A saddle shaped electrostatic potential profile exists in the reconnection plane, and ions are accelerated by the resulting electric field at the separatrices. These accelerated ions are then thermalized by re-magnetization in the downstream region. A quantitative inventory of the converted energy is presented in a reconnection layer with a well-defined, variable boundary. We have also carried out a systematic study of the effects of boundary conditions on the energy inventory. This study concludes that about 50% of the inflowing magnetic energy is converted to particle energy, 2/3 of which is ultimately transferred to ions and 1/3 to electrons. Assisted by another set of magnetic reconnection experiment data and numerical simulations with different sizes of monitoring box, it is also observed that the observed features of energy conversion and partitioning do not depend on the size of monitoring boundary across the range of sizes tested from 1.5 to 4 ion skin depths.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Heating effects on pyrogenic organic matter mass loss and temperature in a sand matrix in a lab setting from 2022

This dataset contains data associated with the preprint “A laboratory method for simulating the effects of subsequent fires on pyrogenic organic matter at different exposure depths in a sand matrix” (https://doi.org/10.1101/2024.07.30.605814), which has been accepted for publication in the International Journal of Wildland Fire. The dataset was generated to investigate the effects of subsequent fires on preexisting pyrogenic organic matter (PyOM) at different soil depths. Specifically, we designed a laboratory method to simulate soil heating from above, applying realistic heat flux profiles (High, Low, Control) derived from jack pine (Pinus banksiana Lamb.) log burns. Using a cone calorimeter, these profiles were applied to buried jack pine PyOM (produced at 350°C) in a sand matrix pyrocosm to examine PyOM mass loss at different depths (surface, 1 cm, 5 cm). Each treatment (e.g., High heat flux at 1 cm depth) was applied once to five replicates in a pyrocosm.The dataset includes mass loss fraction of PyOM and time-series temperature profiles (measured with type K thermocouples). Additionally, calibration datasets for high and low heat flux profiles are provided, including programmed heat flux profile data and three sets of calibration results. All datasets are in .csv format and were analyzed in R using code found at https://github.com/MengmengLuo/Reburning-pyrogenic-organic-matter-A-laboratory-method-for-dosing-dynamic-heat-fluxes-from-above. This dataset supports research on PyOM transformations during subsequent fires, and methodological advancements in simulating soil heating. It can be used to explore fire-induced PyOM alterations across different fire intensities and soil depths.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Multi-fidelity Fourier neural operator for fast modeling of large-scale geological carbon storage

Deep learning-based surrogate models have been widely applied in geological carbon storage (GCS) problems to accelerate the prediction of reservoir pressure and CO2 plume migration. Large amounts of data from physics-based numerical simulators are required to train a model to accurately predict the complex physical behaviors associated with this process. In practice, the available training data are always limited in large-scale 3D problems due to the high computational cost. Therefore, we propose to use a multi-fidelity Fourier neural operator (FNO) to solve large-scale GCS problems with more affordable multi-fidelity training datasets. FNO has a desirable grid-invariant property, which simplifies the transfer learning procedure between datasets with different discretization. Here, we first test the model efficacy on a GCS reservoir model being discretized into 110 k grid cells. The multi-fidelity model can predict with accuracy comparable to a high-fidelity model trained with the same amount of high-fidelity data with 81% less data generation costs. We further test the generalizability of the multi-fidelity model on a same reservoir model with a finer discretization of 1 million grid cells. This case was made more challenging by employing high-fidelity and low-fidelity datasets generated by different geostatistical models and reservoir simulators. We observe that the multi-fidelity FNO model can predict pressure fields with reasonable accuracy even when the high-fidelity data are extremely limited. The findings of this study can help for better understanding of the transferability of multi-fidelity deep learning surrogate models.

58 GEOSCIENCES↗

NASA NTRS2011

New Insights in Tropospheric Ozone and its Variability

We have produced time-slice simulations using the Goddard Earth Observing System Version 5 (GEOS-5) coupled to a comprehensive stratospheric and tropospheric chemical mechanism. These simulations are forced with observed sea surface temperatures over the past 25 years and use constant specified surface emissions, thereby providing a measure of the dynamically controlled ozone response. We examine the model performance in simulating tropospheric ozone and its variability. Here we show targeted comparisons results from our simulations with a multi-decadal tropical tropospheric column ozone dataset obtained from satellite observations of total column ozone. We use SHADOZ ozonesondes to gain insight into the observed vertical response and compare with the simulated vertical structure. This work includes but is not limited to ENSO related variability.

Oman, Luke D.↗

DOE OSTI2022

Soil pH Buffering Capacity, Geochemical Characterization, and Soil Water Retention for Arctic Soils of Seward Peninsula and Utqiagvik, Alaska, 2013-2019

This dataset provides pH titration data and soil pH buffering capacities of 21 Arctic soils that were collected between 2013-2019. Geochemical data including soil organic carbon, carbon:nitrogen ratio, initial pH, and gravimetric water content are also reported. Additional measurements of cation exchange capacity and soil water retention (dry range) are presented for selected soils. A script, developed in R, is also included for a simple biogeochemical simulation that incorporates soil pH buffering capacity. This dataset contains 7 csv files and one R script.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗