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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

How to detect lensing rotation

Gravitational lensing rotation of images is predicted to be negligible at linear order in density perturbations, but can be produced by the post-Born lens-lens coupling at second order. This rotation is somewhat enhanced for Cosmic Microwave Background (CMB) lensing due to the large source path length, but remains small and very challenging to detect directly by CMB lensing reconstruction alone. We show the rotation may be detectable at high significance as a cross-correlation signal between the curl reconstructed with Simons Observatory (SO) or CMB-S4 data, and a template constructed from quadratic combinations of large-scale structure (LSS) tracers. Equivalently, the lensing rotation-tracer-tracer bispectrum can also be detected, where LSS tracers considered include the CMB lensing convergence, galaxy density, and the Cosmic Infrared Background (CIB), or optimal combinations thereof. We forecast that an optimal combination of these tracers can probe post-Born rotation at the level of 5.7σ–6.1σ with SO and 13.6σ–14.7σ for CMB-S4, depending on whether standard quadratic estimators or maximum a posteriori iterative methods are deployed. We also show possible improvement up to 21.3σ using a CMB-S4 deep patch observation with polarization-only iterative lensing reconstruction. However, these cross-correlation signals have non-zero bias because the rotation template is quadratic in the tracers, and exists even if the lensing is rotation free. We estimate this bias analytically, and test it using simple null-hypothesis simulations to confirm that the bias remains subdominant to the rotation signal of interest. Detection and then measurement of the lensing rotation cross-spectrum is therefore a realistic target for future observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Identification and Characterization of Persistent Cold Pool Events from Temperature and Wind Profilers in the Columbia River Basin

Cold pool events occur when deep layers of stable, cold air remain trapped in a valley or basin for multiple days, without mixing out from daytime heating. With large impacts on air quality, freezing events, and es-pecially on wind energy production, they are often poorly forecast by modern mesoscale numerical weather prediction (NWP) models. Understanding the characteristics of cold pools is, therefore, important to provide more accurate forecasts. This study analyzes cold pool characteristics with data collected during the Second Wind Forecast Improvement Project (WFIP2), which took place in the Columbia River basin and Gorge of Oregon and Washington from fall 2015 until spring 2017. A subset of the instrumentation included three microwave radiometer pro?lers, six radar wind pro?lers with radio acoustic sounding systems, and seven sodars, which together provided seven sites with collocated vertical pro?les of temperature, humidity, wind speed, and wind direction. Using these collocated observations, we developed a set of criteria to determine if a cold pool was present based on stability, wind speed, direction, and temporal continuity, and then developed an automated algorithm based on these criteria to identify all cold pool events over the 18 months of the ?eld project. Characteristics of these events are described, including statistics of the wind speed distributions and pro?les, stability conditions, cold pool depths, and descent rates of the cold pool top. The goal of this study is a better understanding of these characteristics and their processes to ultimately lead to improved physical parameterizations in NWP models, and consequently improve forecasts of cold pool events in the study region as well at other locations that experiences similar events.

McCaffrey, Katherine L.↗
DOE OSTI2021

Inclusive and exclusive electron scattering data analysis from Jefferson Lab experiment E12-14-012

Since the first observations of neutrino oscillation, neutrino experiments have come a long way toward precise measurements of the neutrino oscillation parameters, but some obstacles still remain. The next generation of oscillation experiments, including the Deep Underground Neutrino Experiment (DUNE), will be using the Liquid Argon Time Projection Chambers (LArTPCs) with natural argon as the neutrino target material. A precise model of the neutrino cross section on argon does not exist, which is a source of significant uncertainty in such experiments. The E12-14-012 experiment at Jefferson Lab seeks to help remedy this via electron scattering measurements on argon and titanium targets. The experiment collected both inclusive (e,e') and exclusive (e,e'p) data at a wide range of kinematics with the intent to measure the electron-nucleus cross section and thus derive a spectral function for argon that can be used with neutrino experiments. The use of titanium in this experiment stems from the equivalent shell structure that its protons share with the neutrons in argon, which will be crucial in oscillation experiments but cannot be measured directly in electron scattering. This thesis collects several papers which present results from the E12-14-012 experiment. These results include the inclusive (e,e') cross sections for carbon, titanium, argon, and aluminum at a beam energy of 2.22 GeV and a scattering angle of 15.54 deg with uncertainty of less than 5%. Also included are the first results of the exclusive (e,e'p) cross section on argon and titanium.

Murphy, Matthew D.↗
DOE OSTI2026

Machine learning surrogates for ion energy–angle distributions in thermal and RF plasma sheaths

Ion energy–angle distributions (IEADs) at material surfaces are a critical input for plasma–material interaction (PMI) studies in fusion devices, yet they are computationally expensive to obtain using particle-in-cell (PIC) simulations. In this work, we develop a machine learning surrogate based on a deep deconvolutional neural network (DDeCNN) trained on large databases generated with the hPIC2 code. The surrogate is capable of reconstructing IEADs from sheath parameters for both thermal and radio-frequency (RF) plasmas, including cases with multiple ion species. Across thousands of test cases, the model achieves high accuracy, with over 97 % of predictions classified as good or average based on standard error metrics (MAE, MSE, L2). Even in the more challenging RF and multi-species regimes, the surrogate reliably captures the multi-peak structure of PIC results. Once trained, the surrogate produces IEADs in milliseconds on a common workstation, yielding speedups of six to seven orders of magnitude compared with running a full PIC simulation. This computational gain enables dense parameter scans and direct coupling of IEAD predictions with PMI and erosion models on whole-device scales in fusion-relevant conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Ultrawide bandgap semiconductor heterojunction p–n diodes with distributed polarization-doped p-type AlGaN layers on bulk AlN substrates

Ultrawide bandgap heterojunction p–n diodes with polarization-induced AlGaN p-type layers are demonstrated using plasma-assisted molecular beam epitaxy on bulk AlN substrates. Current–voltage characteristics show a turn-on voltage of Vbi≈5.5 V, a minimum room temperature ideality factor of η≈1.63, and more than 12 orders of current modulation at room temperature. A stable current operation of the ultrawide bandgap semiconductor diode is measured up to a temperature of 300 °C. The one-sided n+–p heterojunction diode design enables a direct measurement of the spatial distribution of polarization-induced mobile hole density in the graded AlGaN layer from the capacitance–voltage profile. The measured average mobile hole density is p∼5.7×1017 cm−3, in close agreement with what is theoretically expected from distributed polarization doping. Light emission peaked at 260 nm (4.78 eV) observed in electroluminescence corresponds to interband radiative recombination in the n+ AlGaN layer. A much weaker deep-level emission band observed at 3.4 eV is attributed to cation-vacancy and silicon complexes in the heavily Si-doped AlGaN layer. These results demonstrate that distributed polarization doping enables ultrawide bandgap semiconductor heterojunction p–n diodes that have wide applications ranging from power electronics to deep-ultraviolet photonics. These devices can operate at high temperatures and in harsh environments.

Physics↗
DOE OSTI2021

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Recent advances in biomass pretreatment using biphasic solvent systems

The complexity and recalcitrance of lignocellulosic biomass seriously hinder its subsequent conversion to liquid fuels. To achieve high-value utilization of lignocellulosic biomass, the physical–chemical barrier should be overcome through appropriate pretreatment techniques to improve the accessibility of cellulose for efficient enzymatic hydrolysis. With the rapid emergence of novel pretreatment solvents, biphasic solvent pretreatments represent a nascent and green pretreatment method that has shown outstanding advantages and broad application prospects in the biorefinery of lignocellulosic substrates due to its ability to provide economically viable biomass upgrading, the separation process for products in the solvent phase, and the reutilization of solvents. Herein, different types of biphasic solvents (e.g., 2-methyltetrahydrofuran, methyl isobutyl ketone, 1-butanol, phenoxyethanol, ionic liquids, and deep eutectic solvents) were reviewed systematically, including the fundamental designs of biphasic solvents for biomass pretreatment, their effect on the fractionation of individual biomass components (e.g., carbohydrate and lignin) and the enzymatic hydrolysis performance, and the coproduction of furan and hydroxymethylfurfural. Finally, the main pros and cons of these different biphasic solvent systems are summarized, and the future development direction is also proposed. Finally, this review can provide a reference for designing and selecting effective biphasic pretreatment methods for various types of lignocellulosic biomass.

09 BIOMASS FUELS↗
DOE OSTI2025

Neutrino electromagnetic properties and the weak mixing angle at the LHC Forward Physics Facility

The LHC produces an intense beam of highly energetic neutrinos of all three flavors in the forward direction, and the Forward Physics Facility (FPF) has been proposed to house a suite of experiments taking advantage of this opportunity. In this study, we investigate the FPF’s potential to probe the neutrino electromagnetic properties, including neutrino millicharge, magnetic moment, and charge radius. We find that, due to the large flux of tau neutrinos at the LHC, the FPF detectors will be able to provide more sensitive constraints on the tau neutrino magnetic moment and millicharge than previous measurements at DONUT, by searching for excess in low recoil energy electron scattering events. We also find that, by precisely measuring the rate of neutral current deep inelastic scattering events, the FPF detectors have the potential to obtain the strongest experimental bounds on the neutrino charge radius for the electron neutrino, and one of the leading bounds for the muon neutrino flavor. The same signature could also be used to measure the weak mixing angle, and we estimate that sin 2 θ W could be measured to about 3% precision at a scale Q ∼ 10 GeV , shedding new light on the longstanding NuTeV anomaly. Published by the American Physical Society 2025

Abraham, Roshan Mammen (ORCID:0000000346783808)↗
DOE OSTI2026

Studies of Localization Effects on Transverse Kinematic Imbalance in the GENIE Generator

The forthcoming Deep Underground Neutrino Experiment (DUNE) requires precise modeling of neutrino--nucleus interactions to achieve its neutrino-oscillation measurement goals. GENIE, the Monte Carlo event generator used by DUNE and other Fermilab-based experiments as a central value model for neutrino-nucleus cross sections, exhibits known discrepancies in comparison to MicroBooNE cross section data, especially when considering neutrino scattering events' transverse kinematic imbalance (TKI). For quasielastic-like measurements, the data show a larger transverse missing momentum tail while maintaining a peak similar to that predicted by the baseline GENIE model. Previous attempts at a solution include variations of final-state interaction (FSI) strengths, incorrectly decreasing both the peak and tail, thus leaving the discrepancy unresolved. This work investigates whether inconsistencies between local and global treatments of intranuclear physics contribute to the observed mismodeling. In this work, GENIE FSI routines have been modified to directly utilize intranuclear particle positions, thereby introducing a newly localized momentum treatment in correlation with the nominal density in both the hA and hN intranuclear cascade models for their 2018 and 2025 variants. Meson-exchange-current (MEC) localization was also tested for the hN 2018 model. Comparisons of the intranuclear nucleons' ("scattering center") momentum and its radial dependence validate the localization implementation. However, while such consistent localization produces modest changes in the proton momentum spectrum at low-to-medium momenta, only small changes are observed in the TKI distributions. These changes are insufficient to account for the discrepancy with MicroBooNE data. Although a localized treatment improves the internal consistency of the GENIE model, the origin of the TKI discrepancy remains unresolved by such a solution.

Bulla, Braden [Ctr. Coll., Danville; Fermilab]↗
DOE OSTI2025

Dynamic Metal–Support Interaction Dictates Cu Nanoparticle Sintering on Al 2 O 3 Surfaces

Nanoparticle sintering remains a critical challenge in heterogeneous catalysis. In this work, we present a unified deep potential (DP) model based on the Perdew–Burke–Ernzerhof approximation of density functional theory for Cu nanoparticles on three Al 2 O 3 surfaces (γ-Al 2 O 3 (100), γ-Al 2 O 3 (110), and α-Al 2 O 3 (0001)). Using DP-accelerated simulations, we reveal that the nanoparticle size-mobility relationship strongly depends on the supporting surface. The diffusion of nanoparticles on the two γ-Al 2 O 3 surfaces is almost independent of the size of the nanoparticle, while the diffusion on α-Al 2 O 3 (0001) decreases rapidly with increasing size. Interestingly, nanoparticles with fewer than 55 atoms diffuse several times faster on α-Al 2 O 3 (0001) than on γ-Al 2 O 3 (100) at 800 K while expected to be more sluggish based on their larger binding energy at 0 K. The diffusion on α-Al 2 O 3 (0001) is facilitated by dynamic metal–support interaction (MSI), where Al atoms move out of the surface plane to optimize contact with the nanoparticle and relax back to the plane as the nanoparticle moves away. In contrast, the MSI on γ-Al 2 O 3 (100) and on γ-Al 2 O 3 (110) is dominated by more stable and directional Cu–O bonds, consistent with the limited diffusion observed on these surfaces. Our extended MD simulations provide insight into the sintering processes, showing that the dispersity of the nanoparticles strongly influences the coalescence driven by nanoparticle diffusion. We observed that the coalescence of Cu 13 nanoparticles on α-Al 2 O 3 (0001) can occur in a short time (10 ns) at 800 K even with an initial internanoparticle distance increased to 3 nm, while the coalescence on the two γ-Al 2 O 3 surfaces are inhibited significantly by increasing the initial internanoparticle distance. These findings demonstrate that the dynamics of the supporting surface is crucial to understanding the sintering mechanism and offer guidance for designing sinter-resistant catalysts by engineering the support morphology.

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

A Simple Lagrangian Parcel Model for the Initiation of Summer-time Mesoscale Convective Systems over the Central United States

Mesoscale convective systems (MCSs) account for more than 50% of summertime precipitation over the central United States and have a significant impact on local weather and hydrologic cycle. It is hypothesized that the inadequate treatment of MCSs is responsible for the long-standing warm and dry bias over the central United States in coarse-resolution general circulation model (GCM) simulations. In particular, a better understanding of MCS initiation is still lacking. Here a single-column Lagrangian parcel model is first developed to simulate the basic features of a rising parcel. This simple model demonstrates the collective effects of boundary layer moistening and dynamical lifting in triggering convective initiation and reproduces successfully its early afternoon peak with surface equivalent potential temperature as a controlling factor. It also predicts that convection is harder to trigger in the future climate under global warming, consistent with the results from convection-permitting regional climate simulations. Then, a multicolumn model that includes an array of single-column models aligned in the east–west direction and incorporates idealized cold pool interaction mechanisms is developed. The multicolumn model captures readily the cold pool–induced upscale growth feature in MCS genesis from initially scattered convection that is organized into a mesoscale cluster in a few hours. It also highlights the crucial role of lifting effects due to cold pool collision and spreading, subsidence effect, and gust front propagation speed in controlling the final size of mesoscale clusters and cold pool regions. This simple model should be useful for understanding fundamental mechanisms of MCS initiation and providing guidance for improving MCS simulations in GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Band-edge Engineering to Eliminate Radiation-Induced Defect States in Perovskite Scintillators

Under radiative environments such as extended hard X- or γ-rays, degradation of scintillation performance is often due to irradiation-induced defects. To overcome the effect of deleterious defects, novel design mitigation strategies are needed to identify and design more resilient materials. The potential for band-edge engineering to eliminate the effect of radiation-induced defect states in rare-earth-doped perovskite scintillators is explored, taking Ce 3+ -doped LuAlO 3 as a model material system, using density functional theory (DFT)-based DFT + U and hybrid Heyd–Scuseria–Ernzerhof (HSE) calculations. Furthermore, from spin-polarized hybrid HSE calculations, the Ce 3+ activator ground-state 4f position is determined to be 2.81 eV above the valence band maximum in LuAlO 3 . Except for the oxygen vacancies which have a deep level inside the band gap, all other radiation-induced defects in LuAlO 3 have shallow defect states or are outside the band gap, that is, relatively far away from either the 5d 1 or the 4f Ce 3+ levels. Finally, we examine the role of Ga doping at the Al site and found that LuGaO 3 has a band gap that is more than 2 eV smaller than that of LuAlO 3 . Specifically, the lowered conduction band edge envelopes the defect gap states, eliminating their potential impact on scintillation performance and providing direct theoretical evidence for how band-edge engineering could be applied to rare-earth-doped perovskite scintillators.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluation of Nuclear Spent Fuel Disposal in Clay-Bearing Rock - Process Model Development and Experimental Studies

The DOE R&D program under the Spent Fuel Waste Science Technology (SFWST) campaign has made key progress in modeling and experimental approaches towards the characterization of chemical and physical phenomena that could impact the long-term safety assessment of heat-generating nuclear waste disposition in deep clay/shale/argillaceous rock. International collaboration activities such as heater tests and postmortem analysis of samples recovered from these have elucidated key information regarding changes in the engineered barrier system (EBS) material exposed to years of thermal loads. Chemical and structural analyses of sampled bentonite material from such tests has as well as experiments conducted on these are key to the characterization of thermal effects affecting bentonite clay barrier performance and the extent of sacrificial zones in the EBS during the thermal period. Thermal, hydrologic, and chemical data collected from heater tests and laboratory experiments has been used in the development, validation, and calibration of THMC simulators to model near-field coupled processes. This information leads to the development of simulation approaches (e.g., continuum vs. discrete) to tackle issues related to flow and transport at various scales of the host-rock and EBS design concept. Consideration of direct disposal of large capacity dual-purpose canisters (DPCs) as part of the back-end SNF waste disposition strategy has generated interest in improving our understanding of the effects of elevated temperatures on the EBS design. This is particularly important for backfilled repository concepts where temperature plays a key role in the EBS behavior and long-term performance. This report describes multiple R&D efforts on disposal in argillaceous geologic media through development and application of coupled THMC process models, experimental studies on clay/metal/cement barrier and host-rock (argillite) material interactions, molecular dynamic (MD) simulations of water transport during (swelling) clay dehydration, first-principles studies of metaschoepite (UO 2 corrosion product) stability, and advances in thermodynamic plus surface complexation database development. Drift-scale URL experiments provides key data for testing hydrological-chemical (HC) model involving strong couplings of fluid mixing and barrier material chemical interactions. The THM modeling focuses on heater test experiments in argillite rock and gas migration in bentonite as part of international collaboration activities at underground research laboratories (URLs). In addition, field testing at an URL involves in situ analysis of fault slip behavior and fault permeability. Pore-scale modeling of gas bubble migration is also being investigated within the gas migration modeling effort. Interaction experiments on bentonite samples from heater test under ambient and elevated temperatures permit the evaluation of ion exchange, phase stability, and mineral transformation changes that could impact clay swelling. Advances in the development, testing, and implementation of a spent nuclear fuel (SNF) degradation model coupled with canister corrosion focus on the effects of hydrogen gas generation and its integration with Geologic Disposal Safety Assessment (GDSA). GDSA integration activities includes evaluation of groundwater chemistries in shale formations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Parallel IO Libraries for Managing HEP Experimental Data

The computing and storage requirements of the energy and intensity frontiers will grow significantly during the Run 4 & 5 and the HL-LHC era. Similarly, in the intensity frontier, with larger trig ger readouts during supernovae explosions, the Deep Underground Neutrino Experiment (DUNE) will have unique computing challenges that could be addressed by the use of parallel and accelerated dataprocessing capabilities. Most of the requirements of the energy and intensity frontier experiments rely on increasing the role of high performance computing (HPC) in the HEP community. In this presentation, we will describe our ongoing efforts that are focused on using HPC resources for the next generation HEP experiments. The HEPCCE (High Energy Physics-Center for Computational Excellence) IOS (Input/Output and Storage) group has been developing approaches to map HEP data to the HDF5 , an IO library optimized for the HPC platforms to store the intermediate HEP data. The complex HEP data products are serialized using ROOT to allow for experiment independent general mapping approaches of the HEP data to the HDF5 format. The mapping approaches can be optimized for high performance parallel IO. Similarly, simpler data can be directly mapped into the HDF5, which can also be suitable for offloading into the GPUs directly. We will present our works on both complex and simple data model models.

Bashyal, Amit↗
DOE OSTI2023

Flow Directions and Ages of Subsurface Water in a Salt Marsh System Constrained by Isotope Tracing

Salt marshes are dynamic hydrologic systems where terrestrial groundwater, terrestrial surface water, and seawater mix due to bi-directional flows and pressure gradients. Due to the counteracting terrestrial and marine forcings that control these environments, we do not comprehensively understand water fluxes in these complex coastal systems. To understand the water sources, flow directions, and velocities in salt marsh porewater, we employed a combination of geochemical tracers and analytical models across a hillslope-to-salt marsh continuum in a salt marsh experiencing daily inundation of estuarine surface water (SW) from tides and mixing of fresh seasonal groundwater. We used tritium ( 3 H) as a hydrologic tracer to assess porewater ages and stable water isotope ($\delta$ 2 H and $\delta$ 18 O) analyses to separate isotopically distinct estuarine and terrestrial groundwater across different depths and landscape positions in the study transect. We employed electrical conductivity to constrain the role of source mixing and evapotranspiration in salt marsh hydrology. Salinity and stable isotopes revealed that transpiration, rather than evaporation, increased subsurface water salinity to concentrations above estuarine SW during summer. Elevated salinity at depth indicated that salt marsh subsurface water is recharged during the dry growing season. Seasonal recharge patterns drive long-term deep subsurface water dynamics across the salt marsh, with 3 H ages of 3–7 years, and daily tidal cycles drive short-term shallow porewater dynamics with 3 H ages of 0 ± 3.6 years. Furthermore, our conceptual understanding of the spatiotemporal changes in SW-subsurface water interactions at the terrestrial-marine interface quantifies the hydrological constraints we are missing to improve our understanding of biogeochemical cycles within the salt marsh.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

The Short Life of Upvalley Wind in a High‐Altitude Valley in the Colorado Rocky Mountains

Thermally driven upvalley (UV) wind in the upper East River Valley in the Colorado Rocky Mountains often unexpectedly stops in midmorning and reverses back to downvalley (DV) wind. We use a comprehensive observational data set for a nearly two‐year long period to analyze the wind system and boundary layer evolution in this high‐altitude valley and determine the reason for this early wind reversal. Days with short UV wind predominantly occur during the warm season when the valley floor is free of snow and the convective boundary layer (CBL) grows well above the height of the surrounding ridges. UV wind persists throughout the day only on a few days during the warm season. We link differences in valley wind evolution to wind direction at upper levels at and above ridge height and propose forced channeling mechanisms to describe coupling between valley and upper‐level wind when the CBL grows above ridge height. The frequency distribution of upper‐level wind direction is such that channeling in the DV direction is favored, which explains the predominance of days with short UV wind. The deep CBL is supported by the presence of a deep weakly stably stratified residual layer with high aerosol content, which is regularly present over the mountain range during the warm season. On days when the CBL does not grow above ridge height, for example, when the valley floor is covered by snow, thermally driven UV wind is able to persist throughout the day independent of upper‐level wind direction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deep mitigation of CO 2 and non-CO 2 greenhouse gases toward 1.5 °C and 2 °C futures

Stabilizing climate change well below 2 °C and towards 1.5 °C requires comprehensive mitigation of all greenhouse gases (GHG), including both CO 2 and non-CO 2 GHG emissions. Here we incorporate the latest global non-CO 2 emissions and mitigation data into a state-of-the-art integrated assessment model GCAM and examine 90 mitigation scenarios pairing different levels of CO 2 and non-CO 2 GHG abatement pathways. We estimate that when non-CO 2 mitigation contributions are not fully implemented, the timing of net-zero CO 2 must occur about two decades earlier. Conversely, comprehensive GHG abatement that fully integrates non-CO 2 mitigation measures in addition to a net-zero CO 2 commitment can help achieve 1.5 °C stabilization. While decarbonization-driven fuel switching mainly reduces non-CO 2 emissions from fuel extraction and end use, targeted non-CO 2 mitigation measures can significantly reduce fluorinated gas emissions from industrial processes and cooling sectors. Our integrated modeling provides direct insights in how system-wide all GHG mitigation can affect the timing of net-zero CO 2 for 1.5 °C and 2 °C climate change scenarios.

54 ENVIRONMENTAL SCIENCES↗