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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Vanadate Retention by Iron and Manganese Oxides

Anthropogenic emissions of vanadium (V) into terrestrial and aquatic surface systems now match those of geogenic processes, and yet, the geochemistry of vanadium is poorly described in comparison to other comparable contaminants like arsenic. In oxic systems, V is present as an oxyanion with a +5 formal charge on the V center, typically described as H x VO 4 (3–x)– , but also here as V(V). Iron (Fe) and manganese (Mn) (oxy)hydroxides represent key mineral phases in the cycling of V(V) at the solid–solution interface, and yet, fundamental descriptions of these surface-processes are not available. Here, we utilize extended X-ray absorption fine structure (EXAFS) and thermodynamic calculations to compare the surface complexation of V(V) by the common Fe and Mn mineral phases ferrihydrite, hematite, goethite, birnessite, and pyrolusite at pH 7. Inner-sphere V(V) complexes were detected on all phases, with mononuclear V(V) species dominating the adsorbed species distribution. Our results demonstrate that V(V) adsorption is exergonic for a variety of surfaces with differing amounts of terminal –OH groups and metal–O bond saturations, implicating the conjunctive role of varied mineral surfaces in controlling the mobility and fate of V(V) in terrestrial and aquatic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Modified Vegetation Photosynthesis and Respiration Model (VPRM) for the Eastern USA and Canada, Evaluated With Comparison to Atmospheric Observations and Other Biospheric Models

Atmospheric CO 2 measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO 2 flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO 2 fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non-linear temperature, and water stress. Flux estimates from VPRM, the Carnegie-Ames-Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO 2 observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine-scale atmospheric CO 2 variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain-specific parameters, and high-quality input data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A constraint on historic growth in global photosynthesis due to rising CO 2

Theory predicts that rising CO 2 increases global photosynthesis, a process known as CO 2 fertilization, and that this is responsible for much of the current terrestrial carbon sink. The estimated magnitude of the historic CO 2 fertilization, however, differs by an order of magnitude between long-term proxies, remote sensing-based estimates and terrestrial biosphere models. Here we constrain the likely historic effect of CO 2 on global photosynthesis by combining terrestrial biosphere models, ecological optimality theory, remote sensing approaches and an emergent constraint based on global carbon budget estimates. Our analysis suggests that CO 2 fertilization increased global annual terrestrial photosynthesis by 13.5 ± 3.5% or 15.9 ± 2.9 PgC (mean ± s.d.) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global terrestrial photosynthesis to CO 2 and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The age distribution of global soil carbon inferred from radiocarbon measurements

Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (Δ 14 C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil Δ 14 C and mean age. We found that soil depth is a primary driver of Δ 14 C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of Δ 14 C in surface soil. Integrated to a depth of 1m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate Δ 14 C values that imply younger carbon ages and a more rapid carbon turnover. Overall, our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.

58 GEOSCIENCES↗
DOE OSTI2023

A Large Ensemble Global Dataset for Climate Impact Assessments

We present a self-consistent, large ensemble, high-resolution global dataset of long-term future climate, which accounts for the uncertainty in climate system response to anthropogenic emissions of greenhouse gases and in geographical patterns of climate change. The dataset is developed by applying an integrated spatial disaggregation (SD) - bias-correction (BC) method to climate projections from the MIT Integrated Global System Model (IGSM). Four emission scenarios are considered that represent energy and environmental policies and commitments of potential future pathways, namely, Reference, Paris Forever, Paris 2 °C and Paris 1.5 °C. The dataset contains nine key meteorological variables on a monthly scale from 2021 to 2100 at a spatial resolution of 0.5°x 0.5°, including precipitation, air temperature (mean, minimum and maximum), near-surface wind speed, shortwave and longwave radiation, specific humidity, and relative humidity. We demonstrate the dataset’s ability to represent climate-change responses across various regions of the globe. This dataset can be used to support regional-scale climate-related impact assessments of risk across different applications that include hydropower, water resources, ecosystem, agriculture, and sustainable development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Climate responses in China to domestic and foreign aerosol changes due to clean air actions during 2013–2019

In recent years, to improve air quality, significant efforts have been made to reduce regional aerosols including China, Europe and North America, which have potential impacts on climate. In this study, fast and slow climate responses in China to changes in domestic and foreign anthropogenic emissions of aerosols and precursors from 2013 to 2019 are investigated using the Community Earth System Model version 1 (CESM1). Aerosol emissions changes, especially reductions in China, North America and Europe, during 2013–2019 resulted in surface air temperature increases across the Northern Hemisphere. Global aerosol changes induce a regional warming of 0.2 °C in China, equally contributed by domestic and foreign emissions changes. China’s domestic emission reductions lead to a regional average temperature rise of 0.1°C, primarily driven by rapid atmospheric adjustments over eastern China. Foreign aerosol changes, particularly reductions in North America and Europe, also contributed to a 0.1°C warming in China through slow oceanic processes. This warming in China induced by foreign aerosol changes is due to a teleconnection between the aerosol-induced anomalous regional warming in Eastern U.S.-North Atlantic Ocean-Europe and the downstream East Asian climate through anomalous wave trains propagation. The comparable influence of domestic and foreign aerosol changes on climate in China underscores the importance of international collaboration in climate mitigation endeavors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Carbon mineralization pathways in interfacial adsorbed water nanofilms

Carbon mineralization in humidified carbon dioxide offers a promising route to mitigate anthropogenic emissions in a world stressed by water security. Despite its technological importance, our understanding of carbonation in water-poor environments lags, as traditional dissolution-precipitation pathways struggle to explain the adsorbed water nanofilm-mediated reactivity. Here, we utilize in operando X-ray diffraction (XRD) and advanced molecular simulations to investigate nanoconfined reactions driving forsterite carbonation, the magnesium-rich olivine. By examining magnesium ion dissolution and transport in atomistic simulations of the forsterite-water-carbon dioxide interface and comparing these with the in operando XRD activation energies, we identify both processes as rate-limiting at saturation. Our simulations reveal a mechanistic view of interfacial carbonation, where dissolution and precipitation are mediated by anomalous quasi two-dimensional diffusion. The transport process involves intermittent diffusive hopping in the desorbed state, separated by crawling events that are spatially short but temporally long. This understanding transcends carbon mineralization, with implications for understanding the transport of contaminants in geosystems, the design of multifunctional materials, water desalination, and molecular recognition systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Benchmarking nitrous oxide adsorption and activation in metal–organic frameworks bearing coordinatively unsaturated metal centers

Anthropogenic emissions of N 2 O, the third most abundant greenhouse gas after CO 2 and CH 4 , are contributing to global climate change. Although metal–organic frameworks (MOFs) have been widely studied as adsorbents for CO 2 and CH 4 , less effort has focused on the use of MOFs to remove N 2 O from emission streams or from air. Further, N 2 O activation would enable its use as an inexpensive oxidant for fine chemical synthesis. Here, in this paper, we identify features that contribute to strong binding and high uptake of N 2 O at coordinatively unsaturated metal sites in the M 2 Cl 2 (btdd) (M = Mn, Co, Ni, Cu; btdd 2– = bis(1,2,3-triazolo[4,5-b],[4',5'-i])dibenzo[1,4]dioxin) and M 2 (dobdc) (M = Mg, Mn, Fe, Co, Ni, Cu, Zn; dobdc 4– = 2,5-dioxido-1,4-benzenedicarboxylate) series of MOFs. Combined experimental and computational studies suggest that N 2 O adsorption at open-metal-sites is primarily based on electrostatic interactions, rather than π-backbonding, causing MOFs with more Lewis acidic metal centers to be superior N 2 O adsorbents. As a result, Mg 2 (dobdc) demonstrates strong binding and record-setting N 2 O uptake (8.75 mmol g –1 at 1 bar and 298 K). Using density functional theory (DFT) to characterize reactive intermediates and transition states, we demonstrate that N 2 O activation to form a M(IV)–oxo species and N 2 is thermodynamically favorable in Mn 2 (dobdc) and Fe 2 (dobdc) but appears to be kinetically limited in Mn 2 (dobdc). Our work lays a foundation for understanding N 2 O adsorption and activation in MOFs, paving the way for the design of promising next-generation materials for N 2 O capture and utilization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Haze processing of atmospheric particles during wintertime in the Indo-Gangetic Plains

The impacts of haze on visibility, air quality, and climate are not well quantified due to a lack of understanding of the evolution of the mixing state and phase state of atmospheric particles during haze processing. The variability of the mixing state of atmospheric particles contributes significantly to uncertainties associated with the estimated aerosol radiative forcing. We collected particle samples in January 2018 from the highly polluted Indo-Gangetic plain during hazy conditions to study haze-processed particles. Single particle analysis using multi-modal micro-spectroscopy techniques revealed an abundance (40–70% by number) of potassium-rich sulfate particles from biomass-burning influenced smoke. Tilted view imaging showed that most of the organic particles that had inorganic potassium and sulfate inclusions were liquid-like while those without inclusions were more semi-solid. High-resolution mass spectrometry analysis revealed a significant presence of organosulfates and nitroxy-organosulfates in the morning samples (24%) compared to the afternoon samples (9%), despite higher relative humidity in the afternoon. Overall, our results highlight the significant contribution of both organic and inorganic sulfate to the total particulate sulfur budget during haze processing in winter, when anthropogenic emissions such as household burning, agricultural burning, and vehicular emissions are major contributors to particle mass.

Mathai, Susan [Pacific Northwest National Laborato↗
DOE OSTI2024

Global Energy System Transitions

Energy systems power the world’s economies. They are pivotal to providing sustained economic prosperity that provides the goods and services that humans desire. Climate change is intimately linked with energy systems, because CO2 from fossil fuel use is the most important anthropogenic greenhouse gas emitted to the atmosphere, and cumulative anthropogenic emissions determine Earth’s concentration of CO2. Limiting climate change therefore means that global energy systems must reduce net CO2 emissions to zero and stabilize emissions of other GHGs. We compare energy system pathways as they are currently evolving with alternatives that have the potential to limit climate change over the 21st century. The differences are profound. Here we also discuss some frontier research issues that can provide a better understanding of potential pathways and their implications for decisions makers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Probability of committed warming exceeding 1.5 ∘ C and 2.0 ∘ C Paris targets

Abstract The feasibility of achieving the Paris 1.5 ∘ C target continues to be a complex and hotly debated question. To help resolve this question we calculate probability distributions of the committed warming that would ensue if all anthropogenic emissions were stopped immediately, or at successive future times. We use a simple Earth system model together with a Bayesian approach that incorporates multiple lines of evidence and accounts for known model biases. This analysis reveals a wide range of possible outcomes, including no further warming, but also a 15% chance of overshooting the 1.5 ∘ C target, and 1%–2% chance for 2 ∘ C, even if all emissions had stopped in 2020. If emissions merely stabilize in 2020 and stop in 2040, these probabilities increase to 90% and 17%. The uncertainty arises mainly from that of present forcing by aerosols. Rather than there being a fixed date by which emissions must stop, the probability of reaching either target—which is already below 100%—gradually diminishes with delays in eliminating emissions, by 3%–4% per year for 1.5 ∘ C.

Sherwood, Steven C. (ORCID:0000000174208216)↗
DOE OSTI2021

Atmospheric methane removal: a research agenda

Atmospheric methane removal (e.g. in situ methane oxidation to carbon dioxide) may be needed to offset continued methane release and limit the global warming contribution of this potent greenhouse gas. Because mitigating most anthropogenic emissions of methane is uncertain this century, and sudden methane releases from the Arctic or elsewhere cannot be excluded, technologies for methane removal or oxidation may be required. Carbon dioxide removal has an increasingly well-established research agenda and technological foundation. No similar framework exists for methane removal. We believe that a research agenda for negative methane emissions—‘removal' or atmospheric methane oxidation—is needed. We outline some considerations for such an agenda here, including a proposed Methane Removal Model Intercomparison Project (MR-MIP). This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Exploring Randomly Wired Neural Networks for Climate Model Emulation

Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this paper, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them with their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the serially connected dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired dense layers and assess the impact on model performance for models with 1 million and 10 million parameters. We find that models with less-complex architectures see the greatest performance improvement with the addition of random wiring (up to 30.4% for multilayer perceptrons). Furthermore, of 24 different model architecture, parameter count, and prediction task combinations, only one had a statistically significant performance deficit in randomly wired networks relative to their standard counterparts, with 14 cases showing statistically significant improvement. We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Three-Dimensional Modeling of the Explicit Chemistry of Organic Aerosols: Formation, Removal, and Comparison with Observations

The major goals of the project were to further our understanding of the formation of secondary organic aerosols (SOA) from major anthropogenic and biogenic precursors, and to evaluate the GECKO-A chemistry model in the context of three DOE-led field campaigns (GoAmazon, BBOP, and HI-SCALE) with the long-term objective of improving the treatment of organic gases and particles in three-dimensional regional and global models. The three field campaigns were selected to evaluate and improve specific aspects of GECKO-A: (i) GoAmazon to help quantify interactions of biogenic and anthropogenic emissions; (ii) BBOP to predict the UV-visible absorption properties of biomass burning aerosols (brown carbon) from explicit molecular properties; and (iii) HI-SCALE to evaluate new model components that include aqueous (cloud) processing of organic gases and aerosols.

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

Advancing Measurements and Understanding of the Rate and Structure of Atmospheric Warming

The rate and geographic pattern of atmospheric warming are key indicators of historical climate change and play a role in modulating future changes in Earth’s climate. The past and future evolution of atmospheric temperature is simulated using global climate models while observational estimates of past warming are derived from satellite microwave measurements. Individual climate models simulate widely varying rates of past and future atmospheric warming and most model simulations exhibit greater tropospheric (lowest ~10 km of atmosphere) temperature change than satellite observations between 1979 and 2020. This project examined intermodel differences in the pattern of atmospheric warming and how these differences influence climate feedbacks that can amplify or damp the rate of global surface warming. A key result is that climate model representation of the current climate can influence climate feedbacks and the simulation of future changes in climate. Another focus of this project was to analyze how models respond to different input datasets, such as volcanic aerosols or sea surface temperature (in atmosphere-only simulations). We find that different prescribed inputs can affect simulated changes in atmospheric temperature, even though “input uncertainty” is often unconsidered in model-observational comparisons. A final focus of this research project was to consider the influence of natural internal climate variability on satellite era changes in climate. Although the Earth is warming substantially due to anthropogenic emissions of greenhouse gases, the observed warming rate can be modulated by natural variations in the Earth’s climate. We find that natural climate variability has slowed the rate of tropical tropospheric warming, which explains model-satellite differences in the rate of tropospheric warming.

58 GEOSCIENCES↗
DOE OSTI2023

Arctic Aerosol Sources and Mixing States Field Campaign Report

The Arctic is warming at a faster rate than anywhere else on Earth, with rapidly shrinking sea ice extent transforming the region. Depending on chemical and physical properties, atmospheric aerosols directly scatter and/or absorb radiation, serve as cloud droplet and/or ice crystal nuclei, and/or reduce the reflectiveness of the snow surface, thereby altering the atmospheric energy budget. There is a wide spread in the magnitude of simulated arctic aerosol radiative forcing, and significant differences in aerosol concentration levels and seasonal cycles often exist between models and observations. Increasing local natural and anthropogenic emissions are significant, with uncertain climate impacts due to complex feedbacks. Model evaluations, however, are limited by the dearth of arctic aerosol observations available and an inadequate understanding of arctic aerosol processes. The majority of previous arctic aerosol observations have been made through intensive spring/summer field campaigns, with few intensive measurement studies focused on the fall-winter transition, a period when freeze-up is occurring later and thinning sea ice is resulting in wintertime ice fracturing. Aerosol monitoring at arctic coastal stations has provided knowledge of long-term seasonal trends in aerosol concentrations. The completed ARM field campaign addresses observational and knowledge gaps through detailed aerosol size and chemical composition measurements during the fall-winter transition in the coastal Arctic and through the entire annual cycle in the central Arctic. The observations are improving our understanding of the sources and processes controlling the aerosol population in the rapidly changing Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Mixed Precursor and Future Climate in the Southeast U.S. Field Campaign Report

The generation of atmospheric aerosols is a fundamental process in aerosol-climate interactions, where particles act as nuclei for cloud formation and ultimately influence Earth’s radiative balance. Understanding aerosol precursors, chemical compositions, and formation mechanisms, particularly for secondary organic aerosols (SOAs), is essential for assessing their overall impact on climate. Aerosol-climate interactions are especially important in the Southeastern United States (SE-US), where the atmosphere is strongly influenced by both biogenic and anthropogenic emissions, as well as intense land-atmosphere convective coupling. Current mechanistic representations of SOA formation under ambient conditions are largely extrapolated from laboratory-scale oxidation experiments involving individual volatile organic compounds (VOCs), which are often assumed to behave independently when present in mixtures. However, recent empirical evidence demonstrates that coexisting VOC precursors can undergo previously unexplored molecular-scale interactions that significantly alter SOA physicochemical properties, including mass loading, yield, and water uptake.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A tale of two extremes: Temperature sensitivity of carbon loss from cool and hot soils

Soils represent the largest terrestrial carbon (C) pool, and the flux of carbon dioxide (CO 2 ) from soils to the atmosphere is ~ 6-10 times more than anthropogenic emissions. Understanding responses of soil CO 2 emissions to warming is crucial for evaluating feedback to ongoing environmental changes. The relationship between microbial respiration and temperature is typically modeled using a Q 10 function. Generally, observations of the apparent Q 10 of soil respiration are higher for cold vs. warm ecosystems, reflecting expected biophysical controls of Arrhenius kinetics. However, results from two field warming experiments in the tropics contradict this expectation, both observing extraordinarily high soil respiration responses to in situ warming. Our overall objective for the proposed work is to reduce uncertainty in temperature sensitivity of soil C loss by systematically synthesizing underlying mechanisms related to soil C turnover and stabilization. We are evaluating the temperature sensitivity of soil respiration in ecosystems across temperature extremes (e.g., arctic/boreal and tropical systems) by integrating data collected from field warming experiments with machine learning and biogeochemical models.

54 ENVIRONMENTAL SCIENCES↗