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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Synthetic Soil Aggregates: Bioprinted Habitats for High-Throughput Microbial Metaphenomics

The dynamics of microbial processes are difficult to study in natural soil, owing to the small spatial scales on which microorganisms operate and to the opacity and chemical complexity of the soil habitat. To circumvent these challenges, we have created a 3D-bioprinted habitat that mimics aspects of natural soil aggregates while providing a chemically defined and translucent alternative culturing method for soil microorganisms. Our Synthetic Soil Aggregates (SSAs) retain the porosity, permeability, and patchy resource distribution of natural soil aggregates—parameters that are expected to influence emergent microbial community interactions. We demonstrate the printability and viability of several different microorganisms within SSAs and show how the SSAs can be integrated into a multi-omics workflow for single SSA resolution genomics, metabolomics, proteomics, lipidomics, and biogeochemical assays. We study the impact of the structured habitat on the distribution of a model co-culture microbial community and find that it is significantly different from the spatial organization of the same community in liquid culture, indicating a potential for SSAs to reproduce naturally occurring emergent community phenotypes. The SSAs have the potential as a tool to help researchers quantify microbial scale processes in situ and achieve high-resolution data from the interplay between environmental properties and microbial ecology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modeling Functional Organic Chemistry in Arctic Rivers: An Idealized Siberian System

Rivers of the Arctic will become ever more important for the global climate, since they carry a majority of continental dissolved organic carbon flux into the rapidly changing polar ocean. Aqueous organics comprise a wide array of functional groups, several of which are likely to impact coastal and open water biophysical properties. Light attenuation, interfacial films, aerosol formation, gas release and momentum exchange can all be cited. We performed Lagrangian kinetic modeling for the evolution of riverine organic chemistry as the molecules in question make their way from the highlands to Arctic outlets. Classes as diverse as the proteins, sugars, lipids, re-condensates, humics, bio-tracers and small volatiles are all included. Our reduced framework constitutes an idealized northward flow driving a major hydrological discharge rate and primarily representing the Russian Lena. Mountainous, high solute and tundra sources are all simulated, and they meet up at several points between soil and delta process reactors. Turnover rates are parameterized beginning with extrapolated coastal values imposed along a limited tributary network, with connections between different terrestrial sub-ecologies. Temporal variation of our total dissolved matter most closely resembles the observations when we focus on the restricted removal and low initial carbon loads, suggesting relatively slow transformation along the water course. Thus, channel combinations and mixing must play a dominant role. Nevertheless, microbial and photochemical losses help determine the final concentrations for most species. Chemical evolution is distinct for the various functionalities, with special contributions from pre- and post-reactivity in soil and delta waters. Several functions are combined linearly to represent the collective chromophoric dissolved matter, characterized here by its absorption. Tributaries carry the signature of lignin phenols to segregate tundra versus taiga sources, and special attention is paid to the early then marine behaviors of low molecular weight volatiles. Heteropolycondensates comprise the largest percentage of reactive carbon in our simulations due to recombination/accumulation, and they tend to be preeminent at the mouth. Outlet concentrations of individual structures such as amino acids and absorbers lie above threshold values for biophysical influence, on the monolayer and light attenuation. The extent of coastal spreading is examined through targeted regional box modeling, relying on salinity and color for calibration. In some cases, plumes reach the scale of peripheral arctic seas, and amplification is expected during upcoming decades. Conclusions are mapped from the Lena to other boreal discharges, and future research questions are outlined regarding the bonding type versus mass release as permafrost degrades. Dynamic aqueous organic coupling is recommended for polar system models, from headwaters to coastal diluent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Estimated impacts of forest restoration scenarios on smoke exposures among outdoor agricultural workers in California

Abstract As wildfires continue to worsen across western United States, forest managers are increasingly employing prescribed burns as a way to reduce excess fuels and future wildfire risk. While the ecological benefits of these fuel treatments are clear, little is known about the smoke exposure tradeoffs of using prescribed burns to mitigate wildfires, particularly among at-risk populations. Outdoor agricultural workers are a population at increased risk of smoke exposure because of their time spent outside and the physical demands of their work. Here, we assess the smoke exposure impacts among outdoor agricultural workers resulting from the implementation of six forest management scenarios proposed for a landscape in the Central Sierra, California. We leverage emissions estimates from LANDIS-II to model daily PM 2.5 concentrations with the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and link those to agricultural employment data from the Bureau of Labor Statistics. We find a u-shaped result, in that moderate amounts of prescribed burning result in the greatest reduction in total smoke exposure among outdoor agricultural workers, particularly during months of peak agricultural activity due to wildfire-specific smoke reductions. The reduction in total smoke exposure, relative to scenarios with the least amount of management, decreases as more prescribed burning is applied to the landscape due to the contributions of the fuel treatments themselves to overall smoke burden. The results of this analysis may contribute to preparedness efforts aimed at reducing smoke exposures among outdoor agricultural workers, while also informing forest management planning for this specific landscape.

Environmental Sciences & Ecology↗
DOE OSTI2023

Estimating the economic value of hydropeaking externalities in regulated rivers

Hydropower is a flexible form of electricity generation providing both baseload and balancing power to accommodate intermittent renewables in the energy mix. However, hydropower also generates various externalities. This study investigates individuals' preferences for policies aiming to reduce short-term regulations (i.e., hydropeaking in regulated rivers) while accounting for associated externalities with a discrete choice experiment. This is the first valuation study focusing on hydropeaking that considers both negative and positive externalities. The results imply that most individuals prefer stronger restrictions on short-term regulations to mitigate local environmental impacts. Individuals especially value improvements in recreational use, fish stocks, and the ecological state. On the other hand, potential increases in CO 2 emissions are linked with a clear disutility. The estimated benefits obtained from an improved state of the river environment due to such restrictions exceed the disutility caused by increased CO 2 emissions. Furthermore, the results also reveal unobserved preference heterogeneity among individuals, which should be accounted for in the willingness-to-pay (WTP) estimation using a model specification with correlated utility coefficients. Overall, the findings can inform policy-makers and environmental managers on the economic value of hydropeaking externalities and further guide the sustainable management of rivers regulated for hydropower generation.

13 HYDRO ENERGY↗
DOE OSTI2024

Sustainable valorization of waste tires: Selective hydrotreating for renewable p-cymene production

The escalating global concern over waste tire management driven by the surge in automobiles necessitates sustainable and innovative solutions. Here, this study posits a novel approach by introducing a selective catalytic hydrogenation and dehydrogenation process using a tandem two-stage pressurized fixed-bed reactor, aiming to convert waste tires into valuable sulfur-free p-cymene. The experimental results indicate that among the studied catalysts including Pt/C, Pd/C, Ru/C, and Ni/Al 2 O 3 -SiO 2 , the Pd/C exhibits concomitant hydrogenation and dehydrogenation functionalities, achieving full conversion and displaying 100 % selectivity towards p-cymene from limonene model compound. Furthermore, the Pd/C catalyst demonstrates remarkable efficiency in converting real-world waste tires into p-cymene, yielding up to 134.8 mg/g at optimal conditions. Importantly, this catalyst also facilitates complete hydrodesulfurization activity, addressing environmental concerns by producing sulfur-free liquid products. This innovative method not only optimizes p-cymene synthesis from waste tires but also contributes to environmental sustainability, showcasing both economic and ecological viability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Atmospheric clusters to nanoparticles: Recent progress and challenges in closing the gap in chemical composition

Aerosol nanoclusters (AN), defined here as molecular aggregates suspended in a gas with dimensions between 2 and 10 nm, are the link between substances that we think of as molecules, or the “gas phase,” and those that we consider as particles, or the “condensed phase.” The ability to measure and model the physical and chemical properties of size-resolved AN, which at present is rudimentary at best, is crucial for understanding how particles form and evolve in a number of environments that are natural or influenced by human activities. This review describes the current state-of-the-art for measuring and modeling the size-resolved composition of atmospheric AN. We focus specifically on instruments, many relying on mass spectrometry, that show promise for closing the measurement gap under atmospherically relevant conditions by increasing the size of measurable gas-phase clusters (bottom-up approaches) and by decreasing the size of measurable nanoparticles (top-down approaches). Theoretical methods for predicting AN composition have similarly relied on bottom-up approaches that extend the accuracy of quantum chemistry calculations to larger molecular systems, as well as top-down approaches that correct bulk composition aerosol models for size-dependent properties such as viscosity and volatility. Finally, current measurement and modeling challenges that must be overcome in order to close the gap are discussed.

42 ENGINEERING↗
DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗
DOE OSTI2022

Resolve the Clear-Sky Continuous Diurnal Cycle of High-Resolution ECOSTRESS Evapotranspiration and Land Surface Temperature

Although numerous evapotranspiration (ET) estimates have been developed from remote sensing measurements, a diurnally resolved product with high spatial resolution (<100 m) is still lacking, which is critically needed for agricultural and ecological monitoring. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides, for the first time, ET at 70 m resolution with diurnal samplings, enabling a step toward filling this data gap. This study developed the first framework to resolve the full diurnal cycle of ET and Land Surface Temperature (LST) from sporadic ECOSTRESS measurements. We first constructed 70 m diurnal LST utilizing a diurnal temperature cycle (DTC) model that fused ECOSTRESS and Geostationary Operational Environmental Satellite (GOES) LST. Next, we derived 70 m diurnal ET from the diurnally resolved LST, along with ancillary meteorological and reflectance data sets, using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) algorithm. This framework was tested in two regions: A relatively homogeneous semiarid grassland landscape (centered at the US-Seg Ameriflux site) and a heterogeneous humid cropland landscape (centered at US-ARM). Our diurnally resolved LST and ET successfully reproduced the spatial patterns in the native ECOSTRESS measurements during overpasses for both landscapes (LST: r > 0.97, RMSE ~2°C; ET: r > 0.99, and RMSE = 1–7 W m -2 ). The constructed time series also well captured the temporal variability of the in situ measurements (especially under clear-sky conditions) at both US-Seg and US-ARM, albeit performance varying with availability and quality of ECOSTRESS samples. This study sets the stage for testing and applying our framework to broader climates, biomes, and landscapes toward eventually generating diurnally resolved 70 m global operational LST/ET products to enhance ecological/agricultural applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Pinus ponderosa physiology data from the 2023 girdling manipulation field experiment at Game Ridge, Missoula County, Montana, USA

This dataset contains physiology data on Pinus ponderosa saplings, including: leaf water potential (MPa, megapascals), leaf osmotic potential (MPa, megapascals), leaf pressure potential (MPa, megapascals), leaf relative water content (% saturated mass), as well as soluble sugars (% dry mass),starch (% dry mass), and total nonstructural carbohydrates (% dry mass) found in branch phloem and xylem. Data files and data dictionary(ies) are uploaded as .csv files and .xlsx files. The Users Guide is a .pdf file. Location data can be found in the Google Earth file GameRidge_SitePlotCoordinates.kmz.kml included here. These datasets were collected for Plant Carbohydrate Depletion, Mycorrhizal Networks, and Vulnerability to Drought: An Experimental Test in the Field. This experiment examined the interdependency between plant hydraulics and carbohydrate availability and sought to develop ways to incorporate interactions with below ground symbiotic organisms to better model and quantify forest response to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Rahimi, Stefan (ORCID:0000000331884462)↗
DOE OSTI2021

Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway

Iron is a biochemically critical metal cofactor in enzymes involved in photosynthesis, cellular respiration, nitrate assimilation, nitrogen fixation, and reactive oxygen species defense. Marine microeukaryotes have evolved a phytotransferrin-based iron uptake system to cope with iron scarcity, a major factor limiting primary productivity in the global ocean. Diatom phytotransferrin is endocytosed; however, proteins downstream of this environmentally ubiquitous iron receptor are unknown. We applied engineered ascorbate peroxidase APEX2-based subcellular proteomics to catalog proximal proteins of phytotransferrin in the model marine diatom Phaeodactylum tricornutum . Proteins encoded by poorly characterized iron-sensitive genes were identified including three that are expressed from a chromosomal gene cluster. Two of them showed unambiguous colocalization with phytotransferrin adjacent to the chloroplast. Further phylogenetic, domain, and biochemical analyses suggest their involvement in intracellular iron processing. Proximity proteomics holds enormous potential to glean new insights into iron acquisition pathways and beyond in these evolutionarily, ecologically, and biotechnologically important microalgae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions

The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new machine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are already building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.

Durden, David J.↗
DOE OSTI2023

Towards assessing the impact of anthropogenic sound on fishes: Gaps, perspectives, and a case study of a large floating bridge

Underwater anthropogenic sound can cause physical, physiological, and behavioral impacts on fishes. With the development of marine energy facilities, the impact of man-made underwater sound on fishes has become a growing regulatory concern. Large gaps remain in how to perform an underwater sound assessment. Such gaps mainly reside in scientific evidence, regulatory criteria, sound characterization metrics, sound field modeling, and instrumentation for field measurement. Here, this paper reviewed existing work on underwater sound assessment for fishes to identify critical gaps and provide perspectives on how to deal with these gaps, demonstrated with a case study on the impact of the Hood Canal Bridge traffic sound on migrating steelhead smolts in Washington State, U.S. We also provide perspectives on future research directions to bridge the identified gaps for aquatic ecological assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2020

Intensification of the East Asian summer monsoon lifecycle based on observation and CMIP6

Long-term changes in the East Asian summer monsoon (EASM) lifecycle since 1979 are analyzed based on observational datasets and historical simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6). According to the observation, the active and break phases of EASM have intensified resulting in a shorter but stronger rainy season, followed by a longer dry spell. This intensification in the active-phase precipitation is accompanied by increased lower tropospheric southwesterly wind and subsequent convergence of water vapor flux. These changes are accompanied by the widely reported westward extension of the North Pacific Subtropical High, which has been associated with the warming climate. CMIP6 models generally underestimated the observed intensification of the EASM lifecycle and the monsoon precipitation. However, some of the models did simulate the intensified EASM lifecycle similar to that observed. The result highlights the reasonable performance on EASM shown in some CMIP6 models and those simulations lend support to a dynamically-driven intensification of the EASM lifecycle in the warmer climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hyperspectral remote sensing-based plant community map for region around NGEE-Arctic intensive research watersheds at Seward Peninsula, Alaska, 2017-2019

Using airborne hyperspectral remote sensing data from NASA Airborne Visible-Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) platforms in a region near NGEE-Arctic intensive watersheds at Seward peninsula of Alaska, high resolution (5m) maps of plant community distribution were developed and included in this data collected. AVIRIS-NG data collected over 2017-2019 period were used to develop deep neural networks, trained using vegetation plot observations collected at NGEE-Arctic watersheds at Kougarok, Council and Teller. A hierarchical vegetation classification scheme consisting of six classes at Level I, and 16 classes at Level II contained in two .txt files were used to developed the plant community maps for the region. Two geospatial raster data files (.tif) at both thematic levels are shared in this data collection. Data files in this collection use Alaska Albers Equal Area projection. Readme files available in three formats (*.html, *.md, *.pdf) and one *.png visualization map.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↗