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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Back Trajectories during SAIL

This data set contains air mass back trajectories generated during the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, Stein et al. 2015, Rolph et al. 2017). For each hour from January 1, 2021 to June 30, 2023, a 96-hour back trajectory was initiated at the SAIL sampling site, starting 100 m above ground level. The calculations used the GDAS meteorological data set with model vertical velocity. The files within this dataset show location and meteorological parameters of the back trajectory analysis. There are 21 columns within each data file. The first 2 columns pertain to back trajectory parameters: the trajectory number (which is always 1) and the meteorological grid number. The next 9 columns pertain to spatiotemporal information: the year, month, day, hour (0-23), and minute of the trajectory, the previous time of the trajectory (in negative hours), and the airmass location (latitude, longitude, and height in m AGL). The rest of the parameters within each file pertains to surface and meteorological characteristics of the airmass: pressure (in hPa), potential temperature (in K), temperature (in K), precipitation rate (in mm/hr), mixing depth (in m), relative humidity (in % relative to liquid water), specific humidity (in g/kg), H2O mixing ratio (in g/kg), underlying terrain height (in meters above sea level), and incoming radiation (in W/m^2).

back trajectory↗
DOE OSTI2022

Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions

Abstract. In this study, we develop a watershed zonation approach for characterizing watershed organization and functions in a tractable manner by integrating multiple spatial data layers. We hypothesize that (1) a hillslope is an appropriate unit for capturing the watershed-scale heterogeneity of key bedrock-through-canopy properties and for quantifying the co-variability of these properties representing coupled ecohydrological and biogeochemical interactions, (2) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having the unique distributions of these properties relative to neighboring parcels, and (3) property suites associated with the identified zones can be used to understand zone-based functions, such as response to early snowmelt or drought and solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesize airborne remote sensing data (lidar, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested Butte, Colorado, USA. Results show that (1) we can define the scale of hillslopes at which the hillslope-averaged metrics can capture the majority of the overall variability in key properties (such as elevation, net potential annual radiation, and peak snow-water equivalent – SWE), (2) elevation and aspect are independent controls on plant and snow signatures, (3) near-surface bedrock electrical resistivity (top 20 m) and geological structures are significantly correlated with surface topography and plan species distribution, and (4) K-means, hierarchical clustering, and Gaussian mixture clustering methods generate similar zonation patterns across the watershed. Using independently collected data, we show that the identified zones provide information about zone-based watershed functions, including foresummer drought sensitivity and river nitrogen exports. The approach is expected to be applicable to other sites and generally useful for guiding the selection of hillslope-experiment locations and informing model parameterization.

58 GEOSCIENCES↗
DOE OSTI2024

Invasive Plant Species Management Plan for Los Alamos National Laboratory (Rev.3)

Native species are plants and animals that continually occupy a natural range without direct or indirect introduction and/or care by humans. They are adapted to the environmental conditions and processes of the ecosystem in which they reside. A species introduced into a novel ecosystem can either exploit that ecosystem and thrive or be unable to survive in that ecosystem (Hobbs et al. 2006). Alien or non-native species are species that are intentionally or accidentally introduced into a novel ecosystem and are capable of living and propagating within the physical parameters of that ecosystem. Invasive species is a species that is non-native (or alien) to the ecosystem under consideration and a noxious species are those whose introduction causes or is likely to cause economic or environmental harm. The term invasive species is applicable to plants and animals alike; however, this invasive species management plan currently focuses on invasive vegetation. Invasive plant species are usually capable of rapid colonization of disturbed ground, such as after a change in wildfire regime intensity and frequency (Reilly et al. 2020) or anthropogenic ground disturbance (Burke and Grime 1996; Hobbs and Huenneke 1992). Climate change facilitates the spread and establishment of many alien species and creates new opportunities for them to become invasive (Turbelin and Catford 2021). As climate change impacts increase in the coming decades, we may see in an increase in invasive species establishment. See a list of definitions of terms pertinent to this document in Appendix A: Definitions. Los Alamos National Laboratory (LANL) hosts populations of non-native and invasive species all typical of the northern New Mexico region (Martin 2004; NMDA 2020). By implementing an invasive species management plan, LANL will have readily accessible management strategies for invasive species that are found on-site. The benefits of managing invasive species include a decrease in wildland fire risks, an increase in soil productivity, an increase in (productive or beneficial) wildlife habitat, an increase in water quantity and quality, and the restoration of impacted areas (Burke and Grime 1996; Hobbs and Huenneke 1992; D’Antonio and Hobbie 2005; MacDougall et al. 2013; Reilly et al. 2020). The aforementioned benefits from invasive species management directly enable the LANL mission by ensuring compliance requirements are met and site-wide programs, such as the Vulnerability Assessment and Resilience Plan, are supported for a mutually beneficial outcome. An example for the LANL site specifically, controlling non-native annual plant species, for example, could reduce the costs associated with stabilizing soils during the stormwater pollution prevention compliance process. Roadway and utility right-of-way areas are another place where an integrated vegetation management strategy would promote low growing perennial plants in a way that is mutually beneficial to habitat and the institution through lowered maintenance costs. There is also an economic benefit to managing invasive species. In one nationwide study, invasive plants had an estimated impact cost of $190.45 billion (Fantel-Lepczyk et al. 2022). Investing in preventative measures and surveillance could help to offset future control and management costs of invasive species that have the potential to become established. The State of New Mexico has developed plant species lists and recommendations through the New Mexico Department of Agriculture’s Noxious Weeds Management Act, Article 7D (NM Statute § 76-7D-4 2021); however, effective invasive species management must rely on local knowledge of the site and region. Los Alamos County (LAC) has already compiled a target invasive plant species list and species-specific management objectives (Martin 2004). Therefore, the New Mexico Noxious Weed List and the LAC invasive plant species list, as well as management objectives from those documents, are integrated into LANL’s invasive plant species management plan.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microphysical Piggybacking in the Weather Research and Forecasting Model

Abstract This paper presents incorporation of the microphysical piggybacking into the Weather Research and Forecasting (WRF) model. Microphysical piggybacking is to run a single simulation applying two microphysical schemes, the first scheme driving the simulation and the second piggybacking this simulated flow. “Driving the simulation” means that the simulated microphysical processes, affect the cloud buoyancy and thus force the simulated flow. In contrast, the piggybacking variables are advected by the simulated flow and undergo microphysical transformation, but they do not affect the simulated flow (like in prescribed flow—kinematic—simulations). The two sets of variables (driver and piggybacker) include temperature, water vapor mixing ratio, and all microphysical variables. We provide details of implementing piggybacking into the WRF model, illustrate its applications, and demonstrate the benefits of this methodology in two idealized three‐dimensional cases: (a) a squall line case applying two microphysics schemes, the Thompson bulk microphysics scheme and the University of Pécs/NCAR bin (UPNB) scheme. The piggybacking simulations revealed that the microphysics‐dynamics interaction plays a more important role than the pure microphysical size sorting effect in the transition zone formation. (b) A case of daytime shallow‐to‐deep convective development over land. This case uses the UPNB scheme and contrasts convection developing in environments with either pristine or polluted cloud condensation nuclei (CCN). The piggybacking results indicated that the increase of cloud cover and decrease of supersaturation are mainly associated with the microphysical effect of increasing CCN while the change of precipitation on the ground is also influenced by microphysics‐dynamics interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

AEROICESTUDY-Colorado State University Ice Spectrometer

The dataset comprises measures, using Colorado State University's Ice Spectrometer (IS, an immersion freezing device with a range from 0 °C down to -26 to -29 °C) of atmospheric ice nucleating particle (INP) concentrations taken at ARM's Southern Great Plains (SGP) Site Guest Instrument Facility (GIF), in Lamont, Oklahoma, during the Aerosol-Ice Formation Closure Pilot Study (AEROICESTUDY). A filter sampler was mounted approximately 6 m above ground level atop the GIF rooftop platform. Single-use filter units open to the atmosphere fitted with pre-cleaned and pre-sterilized, 47-mm diameter Nuclepore polycarbonate filters were used. Filters were typically drawn for either a full day period (9AM-5PM CDT) or broken up into morning (9AM-12PM CDT) and afternoon (1PM-5PM CDT) periods. Additionally, 4 overnight filters were taken (6PM-8AM CDT). The volume of air filtered was typically between 3000 and 8000 standard liters for daily samples. After collection, filters were stored at -20 °C until processed. Initial processing to obtain spectra of INP number concentration active via the immersion freezing mechanism versus temperature was conducted using CSU's IS instrument (McCluskey et al., 2018). For measurement of INPs, collected aerosol particles were re-suspended in 8 mL of 0.1 µm-filtered deionized (DI) water. Aliquots of each suspension, and serial dilutions, were dispensed into trays which were fit into aluminum blocks in the IS. Samples were cooled at 0.33 °C per min and the freezing temperatures of wells recorded automatically. Cumulative INP concentrations were determined by first calculating the INPs per mL of suspension based on Vali (1971) and then converting to concentration per standard liter of air using the proportion of the total liquid sample dispensed and the air sample volumes. Two-tailed, 95% confidence intervals for binomial sampling were based on Equation 2 from Agresti and Coull (1998). Aliquots of suspensions from selected samples were also heat treated (95 °C for 20 min) to denature and deactiv

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The toughest animals of the Earth versus global warming: Effects of long‐term experimental warming on tardigrade community structure of a temperate deciduous forest

Abstract Understanding how different taxa respond to global warming is essential for predicting future changes and elaborating strategies to buffer them. Tardigrades are well known for their ability to survive environmental stressors, such as drying and freezing, by undergoing cryptobiosis and rapidly recovering their metabolic function after stressors cease. Determining the extent to which animals that undergo cryptobiosis are affected by environmental warming will help to understand the real magnitude climate change will have on these organisms. Here, we report on the responses of tardigrades within a five‐year‐long, field‐based artificial warming experiment, which consisted of 12 open‐top chambers heated to simulate the projected effects of global warming (ranging from 0 to 5.5°C above ambient temperature) in a temperate deciduous forest of North Carolina (USA). To elucidate the effects of warming on the tardigrade community inhabiting the soil litter, three community diversity indices (abundance, species richness, and Shannon diversity) and the abundance of the three most abundant species ( Diphascon pingue , Adropion scoticum , and Mesobiotus sp.) were determined. Their relationships with air temperature, soil moisture, and the interaction between air temperature and soil moisture were tested using Bayesian generalized linear mixed models. Despite observed negative effects of warming on other ground invertebrates in previous studies at this site, long‐term warming did not affect the abundance, richness, or diversity of tardigrades in this experiment. These results are in line with previous experimental studies, indicating that tardigrades may not be directly affected by ongoing global warming, possibly due to their thermotolerance and cryptobiotic abilities to avoid negative effects of stressful temperatures, and the buffering effect on temperature of the soil litter substrate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Meteorological, reference evapotranspiration and estimated transpiration data, July 2020-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of meteorological data (air temperature, relative humidity, barometric pressure, precipitation, wind speed, soil heat flux, incoming shortwave radiation, incoming longwave radiation, outgoing shortwave radiation and outgoing longwave radiation) from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package also includes reference evapotranspiration calculated via the Penman-Monteith equation and an estimate of willow (Salix spp.) evapotranspiration using the method of Irmak et al., J. Hydrology, 2013. Due to two malfunctioning sensors (incoming shortwave radiation from Oct 2020-May 2021 and incoming longwave radiation from May 2021-March 2022), observed net radiation and calculated evapotranspiration are only included from July-October 2020. However, users may be able to extend calculated evapotranspiration using estimates of these two parameters from other sources. Both sensors have been replaced and data beyond 2021 be uploaded to this repository when it is available.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 3 "*data.csv" files, two for meteorological data for 2020 and 2021 and one for reference evapotranspiration and transpiration data from 2020 ("SLT_OBJ2_PenmanMonteith_2020_data.csv"). Each "*data.csv" file of meteorological data has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding to each measured parameter. We also include a Jupyter notebook ("PenmanMonteithET_Calculations.ipynb") that details the assumptions and equations used to calculate daily and hourly reference evapotranspiration and daily willow transpiration from raw meteorological data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars

At millimeter wavelengths, attenuation by hydrometeors, such as liquid droplets or large snowflakes, is generally not negligible. When using multifrequency ground-based radar measurements, it is common practice to use the Rayleigh targets at cloud top as a reference in order to derive attenuation-corrected reflectivities and meaningful dual-frequency ratios (DFRs). By capitalizing on this idea, this study describes a new quality-controlled approach that aims at identifying regions of cloud where particle growth is negligible. The core of the method is the identification of a “Rayleigh plateau”, i.e., a large enough region near cloud top where the vertical gradient of DFR remains small. By analyzing co-located Ka–W band radar and microwave radiometer (MWR) observations taken at two European sites under various meteorological conditions, it is shown how the resulting estimates of differential path-integrated attenuation (ΔPIA) can be used to characterize hydrometeor properties. When the ΔPIA is predominantly produced by cloud liquid droplets, this technique alone can provide accurate estimates of the liquid water path. When combined with MWR observations, this methodology paves the way towards profiling the cloud liquid water, quality-flagging the MWR retrieval for rain and drizzle contamination, and/or estimating the snow differential attenuation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017

Abstract. Data on irrigation patterns and trends at field-level detail across broad extents are vital for assessing and managing limited water resources. Until recently, there has been a scarcity of comprehensive, consistent, and frequent irrigation maps for the US. Here we present the new Landsat-based Irrigation Dataset (LANID), which is comprised of 30 m resolution annual irrigation maps covering the conterminous US (CONUS) for the period of 1997–2017. The main dataset identifies the annual extent of irrigated croplands, pastureland, and hay for each year in the study period. Derivative maps include layers on maximum irrigated extent, irrigation frequency and trends, and identification of formerly irrigated areas and intermittently irrigated lands. Temporal analysis reveals that 38.5×106 ha of croplands and pasture–hay has been irrigated, among which the yearly active area ranged from ∼22.6 to 24.7×106 ha. The LANID products provide several improvements over other irrigation data including field-level details on irrigation change and frequency, an annual time step, and a collection of ∼10 000 visually interpreted ground reference locations for the eastern US where such data have been lacking. Our maps demonstrated overall accuracy above 90 % across all years and regions, including in the more humid and challenging-to-map eastern US, marking a significant advancement over other products, whose accuracies ranged from 50 % to 80 %. In terms of change detection, our maps yield per-pixel transition accuracy of 81 % and show good agreement with US Department of Agriculture reports at both county and state levels. The described annual maps, derivative layers, and ground reference data provide users with unique opportunities to study local to nationwide trends, driving forces, and consequences of irrigation and encourage the further development and assessment of new approaches for improved mapping of irrigation, especially in challenging areas like the eastern US. The annual LANID maps, derivative products, and ground reference data are available through https://doi.org/10.5281/zenodo.5548555 (Xie and Lark, 2021a).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing the link between aerosol mixing state, structure and composition and their optical properties: Ascension Island as a testbed for the South‐East Atlantic aerosol regimes

Forest and savanna fires are a major source of pollution over the globe. Such fire events emit to the atmosphere a considerable amount of “fluffy” fractal black looking particles that are often referred to as black carbon (BC) or soot aerosols. In fact, BC particles (aerosols) emitted from savanna and agricultural fires over the African continent play an important role in the region’s and the global climate. These fires occur each year between July and October, which is referred to as the biomass burning (BB) season and are considered among the globe's largest man-made emission sources of BC particles. However, during these fire events, not only BC particles are emitted, but also other types of particles and gases that can interact with each other to create new particles, generating a slew of particles often generally referred to as BB aerosols. Although the majority of the fire emissions occur inland in the sub-Saharan part of Africa, their effect reaches far beyond the continent due to the prevailing easterly winds, which transport those aerosols over the South-East Atlantic (SEA) ocean, off the west-coast of Africa. The reason why these particles are so important to the regional and global climate is that they absorb sunlight radiation, which overall warms the climate. However, the magnitude of this warming effect is highly uncertain and depends upon a multitude of aspects. Our overarching goal for this project was to better understand the changes in the aerosol properties as they transport from fire sources downstream toward the ocean and how this can eventually affect the earth's radiative budget in terms of warming or cooling. Following the above, our work here was focused on the investigation of the African BB aerosol plumes mixing state (mixing of BB aerosols and other aerosol types), composition and size distribution as they transport from the African continent towards the SEA ocean, and their link with some of the commonly measurable bulk optical properties such as mass absorption coefficient (MAC) and single scattering albedo (SSA). We utilized three field missions that were conducted over the SEA ocean between 2016 and 2018. Specifically, we used (1) ground-based measurements from the DOE ARM Mobile Facility (AMF1), which was deployed at Ascension Island (ASI) for the LASIC (Layered Atlantic Smoke Interactions with Clouds) campaign between June 2016 and October 2017, (2) airborne measurements from the UK CLARIFY (Clouds and Aerosol Radiative impacts and Forcing) campaign during Aug-Sep-2017, and (3) airborne measurements from the ORACLES (Observations of Aerosols above Clouds and their interactions) campaign during Aug-2017, and Sep-Oct-2018. The three campaigns cover a relatively large region, from the western African coast on the east towards Ascension Island in the middle of the SEA to the west. We used the data gathered by these campaigns to find how BB properties and composition change from near emission sources (the ORACLES campaign flights) downstream (the CLARIFY flights and the ground based LASIC measurements). We used particle trajectory following methodologies to connect the measurements from the different campaigns, which allowed us to follow a certain aerosol plume (airmass) from near-source towards Ascension Island. We found out that atmospheric aging processes governed by the UV light from the sun (photochemistry) and cloud processes (aqueous chemistry) govern the changes seen in the particle composition during the transport were more dominant in dictating aerosol composition than specific emission composition or source types. We assessed how aerosol optical properties change during the BB season and what are the main drivers of this change. We found that the enhanced ratio of BC to CO is well correlated with single scattering albedo (SSA) and mass absorption coefficient (MACBC), providing a simple way to estimate the aerosol optical characteristics in the south-eastern Atlantic Ocean. From the analysis of the location of BB, the primary source fuel, the water content in the fuel, combined with the mean cloud cover and precipitation in the transport areas of the BB plume, we conclude that the increase in BC/CO from June to August is likely to be caused by burning becoming more flaming (hot fires), and the decrease in BC/CO in September and October may be caused by smoldering (colder) fires. We found that aerosol hygroscopicity increases with decreasing altitude below 2km, and that enhanced BB hygroscopicity at lower altitudes is mainly due to a lower organic aerosol (OA) fraction, increased sulfate fraction, and greater hygroscopicity parameter of OA at lower altitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Predicting Large‐Scale Systematic Missing Pipe Attributes in Water Distribution Networks

Water distribution network (WDN) models are an essential tool used by water utilities for hydraulic analysis. Unfortunately, missing data and insufficient resources often make creating and maintaining these models unfeasible. Existing methods to address missing pipe properties, like sequential imputation for missing values and reconstruction using graph metrics, are designed to accommodate random patterns of missing information and require a significant percentage of the system's attributes to be known. However, these data completeness assumptions do not always align with real‐world scenarios where large sections of the WDN model have missing data. To address this challenge, this study proposes a data‐driven approach for estimating pipe diameter when considering different spatial patterns and degrees of data completeness (i.e., 0%–90%). Using data from 16 WDNs in Kentucky, this study compares the use of machine learning (ML) using topological and geospatial features against an existing deterministic approach. Results demonstrate that WDN models with pipe diameters predicted by the proposed ML method had comparable hydraulic performance to the ground truth models. Moreover, results showed that ML method performance varies between WDNs of differing topological classification. Insights from this study help advance the ability to leverage partial data to create and maintain WDN models amid uncertainty and inadequate resources.

Poff, Jason W. [Oregon State Univ., Corvallis, OR ↗
DOE OSTI2023

SAIL Aerosol Vertical Profiles (SAIL-AVP) Field Campaign Report

Aerosols are central to understanding the water cycle within mountainous regions, but a complete understanding of this role cannot be provided without vertically resolved observations, particularly for aerosol-cloud interactions, since we simultaneously need to know aerosol information and meteorology near cloud bases. The goal of SAIL Aerosol Vertical Profiles (SAIL-AVP) was to perform tethered balloon system (TBS) measurements during different seasons to elucidate process-level understanding of the aerosols and associated meteorological conditions within complex mountainous terrain. Measurements were conducted during the deployment of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s second Mobile Facility (AMF2) as part of the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River Watershed of the Upper Colorado River Basin (UCRB) in southwestern Colorado. In mid-latitude continental interior mountain ranges, and especially at SAIL, aerosol vertical profiles and their relationships with meteorology are, almost without exception, inferred from a combination of ground-based and remote-sensing data. These flights provided many more dimensions to aerosol-cloud interactions that are unavailable to those inferential approaches: the TBS collected in situ information in the vertical, optical, chemical, and biological dimensions for aerosol-cloud interactions. Furthermore, the collocation of these flights with longstanding collaborative resources in the region, including the ongoing surface and subsurface hydrologic observations from the DOE’s Watershed Function Science Focus Area (SFA), produced a unique set of atmospheric observations that are complemented by existing land-surface and subsurface (e.g., groundwater) observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

The environmental impacts of global warming driven by methane (CH 4 ) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH 4 . Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH 4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH 4 as a classification problem and (ii) predict the intensity of CH 4 as a regression problem. The classification model performance for CH 4 detection was evaluated using accuracy, F1 score, Matthew’s Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH 4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH 4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.

Majumder, Reek↗
DOE OSTI2020

Carbonyl Sulfide (OCS): Detections in Comets C/2002 T7 (LINEAR), C/2015 ER61 (PanSTARRS), and 21P/Giacobini–Zinner and Stringent Upper Limits in 46P/Wirtanen

Carbonyl sulfide (OCS) is one of the sulfur-bearing molecules detected in different astronomical environments, including comets. The present-day sulfur chemistry in comets may reveal much about the origin of these ices and their subsequent processing history. Cometary sulfur molecules such as H{sub 2}S, H{sub 2}CS, SO{sub 2}, SO, CS, CS{sub 2}, S{sub 2}, and NS have been detected in many comets. However, OCS, the only sulfur-bearing species with fluorescence emission lines at infrared wavelengths, is under-represented in comet volatile studies, having been reported in only six comets so far. We targeted OCS with the NASA Infrared Telescope Facility in comets 46P/Wirtanen, 21P/Giacobini–Zinner, and C/2015 ER61 (PanSTARRS) in 2017–2018 using the high-resolution iSHELL spectrograph, and in C/2002 T7 (LINEAR) in 2004 using the heritage CSHELL spectrograph. In comet C/2015 ER61, the OCS abundance was similar to those measured in bright comets such as comets C/2012 S1 (ISON) and C/1996 B2 (Hyakutake), whereas in C/2002 T7 it was relatively depleted. Our OCS measurement in 21P/Giacobini–Zinner is the first definitive detection of this molecule in a Jupiter-family comet from a ground-based facility and is close to the average OCS abundance determined in comet 67P/Churyumov–Gerasimenko by the Rosetta mission. Our 3σ upper limit for comet 46P/Wirtanen is the lowest reported OCS abundance in any comet. We present production rates and mixing ratios (with respect to H{sub 2}O) for these comets and place our results in the context of comets measured to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Baselining the Indirect Effect by Improving Quantification of Sea Spray and Marine Sources at Ascension Island (Final Report)

Oceans cover two-thirds of the Earth and understanding the interactions of aerosols with clouds in these large marine regions requires quantifying the man-made contributions to the budget of cloud-drop forming particles (known as cloud condensation nuclei, or CCN) relative to the non-manmade “baseline” conditions and understanding the meteorology of boundary layer clouds. Modeling studies have shown substantial uncertainties and sensitivities to natural marine CCN sources, meaning that to reduce uncertainties in indirect effects we must be able to better quantify the CCN budget in ocean regions. While models provide important constraints on these uncertainties, actually reducing uncertainties requires substantial observations in open-ocean and coastal regions in order to establish the baseline on which manmade emissions are added. The tropical South Atlantic Ocean is one of the least-sampled regions of the planet, making the comprehensive measurements of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign provides the longest record of aerosol size distribution measurements from a differential mobility analyzer in a cloud-influenced marine location in the ARM database, with 17 months of ground-based aerosol size distribution measurements. This project addressed the research topic of Aerosol-Cloud Interactions (ACI) by supporting three publications from the LASIC measurements: 1. The ARM measurements were combined with a new value-added technique that was pioneered by the Russell group for quantifying sea salt. This quantification of sea salt uses supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available, observationally-constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available. 2. The fitted modes of the distribution were used to investigate the signatures of cloud processing for very clean to very smoky aerosol conditions, revealing not only differences in the particles that activate in clouds but also in the mechanisms that control that droplet formation process. In clean air, the size required to form a cloud droplet is influenced by the number of particles, as well as how quickly particles take up water during growth in cloud. 3. Building on this aerosol characterization, cloud and meteorological conditions were used to evaluate aerosol-related changes in cloud albedo and optical depth. We introduced a new method of decomposing the impact of aerosols on clouds known as the Twomey effect by incorporating retrieved supersaturation from two independent sets of observations to constrain the feedback of aerosol particles on cloud properties. The method quantifies the reduction of the Twomey effect at high aerosol concentrations, which has never been explained quantitatively by observations. In addition, the results provide the first direct validation for the conditions observed in the tropical South Atlantic of a parcel-based approach that is embedded in many climate models. Together these findings illustrate how ARM extended field campaigns in stratocumulus-covered regions can be used to constrain ACI processes with direct observations, providing specific radiative effects without models. By making such process-specific constraints available to improve ACI in global climate models, ARM observations play a key role in supporting model development.

58 GEOSCIENCES↗
DOE OSTI2021

Boundary Layer Climatology at ARM Southern Great Plains

Operational since 1992, the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, USA, has become a reference research site for meteorological studies. Because of an open data policy the ARM data are used by researchers all over the world. In this report, we review the long-term climatology of the atmospheric boundary layer, the SGP instrumentation, the site and some site-specific atmospheric conditions, which potentially affect wind turbines in the region. Because the atmospheric boundary layer is bounded and influenced by the land surface, observations of surface radiation components and heat fluxes are crucial to understanding land–atmosphere interactions. The entrainment of air, updrafts, downdrafts, and boundary layer height characteristics is needed for understanding the structure and growth of the atmospheric boundary layer. Therefore, measurements from both ground surface in situ and remote-sensing observations at the SGP site provide an overall climatology and their interactions from ground surface up to the boundary layer. Measurements from a 60 m meteorological tower, surface flux stations, disdrometers, soil temperature and moisture flux plates, coherent Doppler lidar, Raman lidar, radiosondes, and satellite data at the SGP central facility were analyzed. All the measurements were generally made within a few square kilometers of each other at the central facility. This report focuses on data from January 2010 to June 2020 at the SGP central facility. The various sections describe the ARM SGP site and surrounding wind turbines; in situ and remotesensing instrumentation used in the report; mathematical equations to analyze fluxes, turbulence, and other boundary layer parameters; a climatological analysis of surface winds, fluxes and thermodynamic parameters for several years; an analysis of observed winds in the framework of Monin-Obukhov Similarity Theory (MOST); an analysis of the boundary layer winds and direction from a Doppler lidar; multi-year turbulence estimates through the boundary layer from a Doppler lidar; atmospheric boundary layer water vapor and relative humidity profiles from Raman lidar; cloud base height and boundary layer height from multiple sensors and satellite data; and finally site-specific atmospheric conditions, such as nocturnal low-level jets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating EAMv2 Simulated High Latitude Clouds Using ARM Measurements in the Northern and Southern Hemispheres

Here, this study evaluates high latitude stratiform mixed-phase clouds (SMPC) in the atmosphere model of the Energy Exascale Earth System Model version 2 (EAMv2) by utilizing one-year-long ground-based remote sensing measurements from the Atmospheric Radiation and Measurement (ARM) program. A nudging approach is applied to model simulations for a constrained comparison with the ARM observations. Observed and modeled SMPCs are sampled and collocated to address the difference in data resolution, so that we can consistently evaluate their macrophysical properties at the North Slope of Alaska (NSA) site in the Arctic and the McMurdo (AWR) site in the Antarctic. We found that EAMv2 overestimates SMPC frequency of occurrence at both sites. However, the model captures the observed larger cloud frequency of occurrence at the NSA site. For collocated SMPCs, the annual statistics of observed cloud macrophysics are generally reproduced at the NSA site, while at the AWR site, there are larger biases. Compared to the AWR site, the lower cloud top and cloud base and the warmer cloud top temperature observed at NSA are well simulated. On the other hand, simulated cloud phase is substantially biased. The model largely overestimates liquid water path, and the ice water path is underestimated at NSA, but at AWR, the liquid water path is frequently underestimated due to the dominance of snow in Antarctic SMPCs. As a result, the observed hemispheric difference in cloud phase partitioning is misrepresented in EAMv2. This study implies that additional model development is needed for high latitude mixed-phase clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Custom-trained Machine-learning Interatomic Potentials: ZnCl2 Aqueous Solution

This dataset was generated using an iterative active-learning strategy implemented in the ArcaNN software package (https://github.com/arcann-chem/arcann_training) to train machine-learning interatomic potentials for aqueous ZnCl2 solutions. Each active-learning cycle consisted of three stages: training, exploration, and labeling. The initial training set combined configurations generated in this work from enhanced-sampling ab initio molecular dynamics simulations with configurations from a previously reported neural-network-potential study of aqueous ZnCl2. The enhanced-sampling ab initio molecular dynamics simulations involved Zn–Cl separation and the chloride coordination number around Zn²? as collective variables. These configurations served as the seed dataset. Subsequent active-learning cycles expanded the training set by identifying and labeling configurations that were poorly represented by the current models, thereby improving coverage of ion-association states and changes in local coordination and charge-state environments relevant to the solution free-energy landscape. For all selected configurations, single-point calculations of the total energies and atomic forces were performed within density functional theory using the CP2K Quickstep module. Reference calculations employed the revPBE-D3 and r2SCAN exchange-correlation functionals. Motivated by recent work on aqueous Zn²?, the main revPBE calculations omitted D3 dispersion contributions involving Zn²?, while retaining the D3 correction for water and chloride. For comparison, fully dispersion-corrected revPBE-D3 reference calculations were also performed, with D3 applied to all species, including Zn²?. Valence electrons were treated explicitly, while core electrons were represented using norm-conserving Goedecker–Teter–Hutter pseudopotentials. The wave functions were expanded using the mixed Gaussian-and-plane-wave scheme with TZV2P-MOLOPT basis sets for all elements and a 600 Ry auxiliary plane-wave cutoff for the electron density. Self-consistent-field convergence was accelerated using the orbital-transformation and Direct Inversion in the Iterative Subspace algorithms, with a convergence threshold of 10?6. All single-point calculations were performed in periodic orthorhombic cells. The CELL_REF keyword in CP2K was used to define a fixed reference cell with a box length of 25 Å. This treatment ensured a consistent reference for configurations extracted from NpT trajectories with fluctuating cell dimensions. The resulting DFT energies and atomic forces constitute the ground-truth labels used to train the MLIPs. The resulting MLIP was trained for aqueous ZnCl2 solutions spanning concentrations from 0 to 30 molal and a broad pH range, from strongly acidic to strongly basic conditions. Representative examples of configurations included in the MLIP training dataset are provided below. These include 1) Representative configurations from the dataset labeled at the revPBE-D3 level, with D3 dispersion interactions involving Zn2+ excluded (revPBE-wo-D3). 2) Representative configurations from the dataset labeled at the fully dispersion-corrected revPBE-D3 level, with D3 interactions applied to all species, including Zn2+ (revPBE-D3). 3) Representative configurations from the dataset labeled at the r2SCAN level of theory (r2SCAN).

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗