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DOE OSTI2024

On the Prediction of Aerosol-Cloud Interactions Within a Data-Driven Framework

Aerosol-cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration N c from aerosol number concentration N a and ambient conditions using a data-driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate N c . We show that the campaign-wide N c can be successfully predicted using machine learning models despite the strongly nonlinear and multi-scale nature of ACI. However, the observation-trained machine learning model fails to predict N c in individual cases while it successfully predicts N c of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data-driven framework, the N c prediction is uncertain at fine spatiotemporal scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

High Semi-Volatile Organic Aerosol Contributions Associated with Ammonium Nitrate in the Coastal Urban Environment

Organic components often contribute 50% or more of the submicron aerosol mass in coastal urban environments, but their partitioning between the gas and particle phases is controlled by a complex mixture of unidentified organic compounds that are poorly constrained by observations. This study compares daily filter organic functional groups (OFG) with online organic mass fragments from La Jolla, California, as part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), quantifying the contributions of four types of non-volatile (NV) organic emission sources to the submicron composition. Daily filters retained only 0.79–0.98 µg/m3 NV submicron organic mass concentration, even though 1.8–1.9 µg/m3 non-refractory (NR) submicron organic mass concentration was measured online. The 62–64% of measured NR submicron organic mass concentration that exceeded what remained on the filters after 23-hr sampling is interpreted as semi-volatile, consistent with the moderate correlation of the NR NV difference to NR ammonium, NR nitrate, and biomass burning-related NV and NR organic factors. The association between semi-volatile organic components and ammonium nitrate likely results from both co-emission and co-evaporation. Size-resolved filter analysis showed that NV organic mass concentration accounts for 68% of NR organic mass concentration for 0.5–1 µm dry diameter but account for 9.0% for 0.18–0.5 µm dry diameter showing the differences in volatility between particle modes. Importantly, volatility of organic components was size-dependent Information Classification: General and associated with ammonium nitrate and biomass burning, providing guidance for constraining atmospheric aerosol properties in global models.

Pelayo, Christian↗

DOE OSTI2026

Statistical Uncertainty of Inhalation Dose Coefficients: Impact of Particle Deposition in ICRP 66 Human Respiratory Tract Model

Inhaled radioactive materials can pose a long-term health concern, as the material can be incorporated into the body’s metabolic pathways and remain in organs and tissues for extended durations. During the retention period, the radioactive material may localize in a source organ and irradiate adjacent target organs and tissues. Distribution of these materials changes over time, requiring biokinetic modeling to evaluate their movement through various tissues and organs. The evolving distribution depends on multiple inputs characterizing the inhaled material, such as particle size and size distribution, particle density, aspect ratio, specific radionuclide, the chemical form, and solubility. In addition, biological parameters such as breathing rate, breathing type (nasal or nasal/oral), respiratory system morphometry, tidal volume, functional residual capacity, and anatomical dead space all influence material transport. These aerosol properties and physiological characteristics of the respiratory tract jointly define a range of initial conditions that influence the time-dependent distribution of radioactive material. To evaluate both uncertainty in the initial conditions of inhalation exposure and the final output (committed effective dose) from biokinetic models, a Python-based software tool, Radiological Exposure Dose Calculator (REDCAL), was developed to propagate uncertainty within the human respiratory tract model. Focusing on deposition fraction uncertainty, the primary objective was to characterize the initial activity distribution across respiratory regions as a function of anticipated particle sizes and distributions. The impact of the deposition fraction uncertainty was propagated to committed effective dose coefficients for selected radionuclides in a companion publication. For each particle size, a lognormal distribution, characterized by its geometric mean as defined within ICRP Publication 66, serves as the basis for introducing uncertainty into the physical processes governing deposition in various lung regions. Finally, this study addresses the deposition process and examines how uncertainty in deposition mechanisms affects activity distribution in the airways, ultimately presenting the expected range and standard deviation of deposited activity as a function of particle size.

International Commission on Radiological Protectio↗

DOE OSTI2023

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k -means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Macquarie Island Cloud and Radiation Experiment (MICRE) Field Campaign Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility, the Australian Antarctic Division (AAD), and the Australian Bureau of Meteorology (BOM) collaborated in deploying a variety of ground instrumentation to Macquarie Island between March 2016 and March 2018. Macquarie Island is located at 54.5° S, 158.9° E and is well situated for studying clouds over the Southern Ocean. The primary objective of the March 2016 to March 2018 deployment, hereafter the Macquarie Island Cloud and Radiation Experiment (MICRE), was to collect observations of surface radiation, cloud, precipitation, and aerosol properties. This report describes the instruments deployed and data sets collected, along with a summary of key data quality issues and information on some value-added products that are being developed to assist scientists in using the data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

ARM Mobile Facility Leaving Alaska for U.S. Southeast

The Atmospheric Radiation Measurement (ARM) user facility will soon begin the process of moving its third mobile facility (AMF3) from Alaska to the Southeastern United States. A workshop in August 2018 identified the Southeast as a priority region of interest for further study by ARM. The Southeast is a region with abundant atmospheric convection, yet the land surface, boundary layer, vegetation, and aerosol properties are markedly different from those seen at ARM’s fixed observatory in Oklahoma, allowing opportunities to explore new research questions. To maximize the scientific value of the new site, DOE’s Biological and Environmental Research program selected a site science team that will help guide the move of AMF3. Brookhaven National Laboratory (BNL) in New York will lead the AMF3 relocation effort. Supported by ARM and DOE’s Atmospheric System Research (ASR), the multi-institutional team is responsible for developing a science plan and initial research project for the Southeastern U.S. deployment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Enhanced Atmospheric Research at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) (Final Report)

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was an international scientific expedition into the central Arctic sea ice to study the changing atmosphere-ice-ocean system and its key physical, chemical, and biological processes in a way that will support improved understanding and modeling of Arctic climate. The US Department of Energy’s Atmospheric Research Measurement (ARM) Program operated its second ARM Mobile Facility (AMF2) as a major contribution to the expedition, and the Atmospheric System Research (ASR) program supported this project to maximize the success of DOE’s involvement in MOSAiC. This project has enabled the successful collection of data through support of field operations plans, in-field operational implementation, oversight of real-time data quality, and representing DOE’s interests within the broader international MOSAiC endeavor. The project has promoted scientific research by facilitating and leading a number of efforts to develop value-added products that will support research, including products related to aerosol properties, trace gases, cloud properties, snowfall, the surface energy budget, and a merged product designed for model assessment. These products and related analyses have also contributed to numerous scientific publications on related themes as well as direct model evaluation. Lastly, the project has engaged in a great deal of communication and outreach activities that help to publicize and promote both MOSAiC and DOE’s specific contribution to a broad audience that includes the scientific community, program management, policy makers, and the general public.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Tethered Balloon System Merged Data (TBSMERGED) Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Tethered Balloon System Merged Data (TBSMERGED) Value-Added Product (VAP) integrates data from instruments flown on ARM’s tethered balloon system missions that collect in situ measurements of temperature, humidity, wind speed, wind direction, and aerosol properties with estimates of cloud base and boundary-layer height from a surface-based ceilometer to improve the ease of use of tethered balloon system (TBS) data sets. The TBSMERGEDINCLOUD VAP, a variation of TBSMERGED, includes supercooled liquid water content (tbsslwc) measurements collected within the cloud.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-Uncrewed Aircraft System (TRACER-UAS) Field Campaign Report

Convective processes drive the development of clouds across a variety of scales, thereby playing a central role in governing weather and climate globally. These processes help to set the stage for circulations that redistribute heat and moisture throughout the lower atmosphere, and simultaneously develop storms that supply precipitation to regions that depend on such rain events for supporting a variety of societal needs. At the same time, extreme precipitation events can result in localized flooding of low-lying areas, posing hazards to transportation and regional inhabitants, and potentially damaging structures and infrastructure. As a result, there is an inherent need to better understand the drivers and modulators of clouds and precipitation and leverage such understanding to make projections of how these important systems may evolve with a changing climate. With nearly three billion of Earth’s human inhabitants living within 200 km of a shoreline, development of such understanding is particularly important in coastal regimes. Central drivers of convective cloud development and life cycle in coastal environments include synoptic forcing, local meso- and microscale circulation regimes, microphysical evolution, and aerosol properties. Atmospheric boundary layer (ABL) development and its evolution can help drive the formation and organization of convective clouds, which then impart their own controls through dynamic processes related to latent heating and cooling (including cold-pool formation and propagation), three-dimensional radiative effects, and background environmental conditions (Fan et al. 2016). Such clouds have been contributing to record precipitation events in southeast Texas in recent years, relative to the last several decades of data from regional precipitation gauges (Fagnant et al. 2020). The greater Houston urban area has experienced particularly large increases in the frequency of extreme rainfall events, with enhanced intensity of extreme events (stretching of distribution tails) having potentially significant implications for watershed floodplain planning and mapping in greater Houston and Harris County. While the most extreme precipitation events were associated with tropical cyclones (e.g., Tropical Storm Allison and Hurricane Harvey; Blood 2014, Kao et al. 2019), strong thunderstorms can also deliver large amounts of precipitation over the area over relatively short periods.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Bankhead National Forest Fact Sheet

By the end of 2023, the Atmospheric Radiation Measurement (ARM) user facility plans to open a new atmospheric observatory in northwestern Alabama. For at least five years, the Bankhead National Forest (BNF) atmospheric observatory will collect data for researchers to learn more about aerosols, clouds, and land-atmosphere interactions in the Southeastern United States. In turn, their studies will lead to improvements in models of the Earth’s climate. A U.S. Department of Energy (DOE) workshop in August 2018 identified the Southeast as a priority region of interest for further study by ARM, a DOE Office of Science user facility that operates fixed-location and mobile observatories worldwide. The Southeast experiences high humidity, frequent convection, and high biogenic emissions. The land surface, boundary layer, vegetation, and aerosol properties across the region are markedly different from those seen at ARM’s long-term observatory in Oklahoma, allowing opportunities to explore new research questions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Tethered Balloon System merged (TBSmerged) Value-Added Product Report

TBSmerged integrates data from instruments flown on ARM’s Tethered Balloon System missions that collect in situ measurements of temperature, humidity, wind speed, wind direction, and aerosol properties with estimates of cloud base and boundary layer height from a surface-based ceilometer to improve the ease of use of TBS datasets. TBSmergedincloud includes supercooled liquid water content (tbsslwc) measurements collected within the cloud.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

2024 Atmospheric Radiation Measurement (ARM) Annual Report

This report provides an overview of the Atmospheric Radiation Measurement (ARM) user facility and a sample of achievements for fiscal year 2024 (FY2024). Strong collaborations between nine DOE national laboratories enable ARM to successfully operate in remote locations around the world. This unique partnership supports the DOE mission to provide for the energy security of the nation. Without the support of the following laboratories, ARM would not be the state-of-the-art facility that it is today. ARM is a multi-laboratory, U.S. Department of Energy (DOE) Office of Science user facility and a key contributor to national and international atmospheric and climate research efforts. ARM offers scientists cutting-edge, ground-based observatories, aerial observation capabilities, and high-performance computing. ARM’s capabilities have enabled more than 30 years of continuous measurements of cloud and aerosol properties and their effects on Earth’s energy balance.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Edge at the Pier: EPCAPE Software-Defined Sensing Field Campaign Report

The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was aimed to enhance the understanding of cloud and aerosol properties in the region surrounding La Jolla, California. To address challenges in data collection and processing from various instruments, an edge computing device known as Waggle Sage Node (WSN) was deployed at the Ellen Browning Scripps Memorial Pier. WSN is a distributed-sensing platform designed to collect and analyze environmental data at the edge. Sage is a multi-agency-supported project that designs and builds a new kind of national-scale reusable cyberinfrastructure to enable artificial intelligence (AI) at the edge based on the Waggle platform. Sponsors include the U.S. Department of Energy (DOE) Advanced Scientific Computing Research (ASCR), DOE National Nuclear Security Administration (NNSA), DOE Biological and Environmental Research (BER) through DOE Artificial Intelligence for Earth System Predictability (AI4ESP), Argonne Laboratory-Directed Research and Development (LDRD). Sage (https://sagecontinuum.org/) is funded as a National Science Foundation Mid-Scale Research Infrastructure (MSRI) project (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935984). This robust, multi-architecture edge computing platform facilitated environmental monitoring during the campaign. This report details the scientific objectives, deployment process, and key results of integrating Waggle into the EPCAPE field campaign.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Using MARCUS, MICRE, and COMBLE data to improve understanding and modeling of cloud, aerosol, and boundary layer processes at high-latitudes

Because it is believed general circulation (GCM) and numerical weather prediction (NWP) models underestimate shortwave radiation over the Southern Ocean (SO) due to inadequate representations of boundary layer (BL) and cloud processes, it is critical to improve our understanding of key aerosol, cloud, precipitation, and BL processes. Over the north Atlantic Ocean (NA), cold-air outbreaks are common, yet few studies of the aerosol and environmental controls of the associated convective BL clouds exist as needed to develop and evaluate GCM representations. Although processes cannot be observed, cloud and aerosol properties can be measured in-situ or remotely retrieved, which when combined with numerical simulations enable process level understanding required to improve model representations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Agricultural Ice Nuclei at Southern Great Plains Supplemental Sampling (AGINSGP-SUPP) Field Campaign Report

The Agricultural Ice Nuclei at Southern Great Plains Supplemental Sampling (AGINSGP-SUPP) field campaign took place from April 8 to April 28, 2022. It included sample collection and analysis of filter samples for ice-nucleating particle (INP) concentrations. By enabling quantification of INPs at warmer temperatures (i.e., T > 258 K), these samples complemented the real-time observations of INPs that were performed as part of the AGINSGP campaign during the same period. In addition to collecting ground-based samples, the campaign collected vertical samples of INPs aboard the Atmospheric Radiation Measurement (ARM) tethered balloon system (TBS) as shown in Figure 1. These vertical samples complemented the vertical profiling of aerosol properties that was supported under a related Facilities Integrating Collaborations for User Science (FICUS) proposal. The overall aim of these three campaigns was to improve understanding of the sources and variability in observed INP concentrations at ARM’s SGP observatory. For example, warm-temperature INPs in this agricultural region are thought to originate in part from regional emissions of fertile, agricultural soils containing high organic content. Laboratory experiments have shown that soil particles with high organic content are more efficient INPs than inorganic soils (e.g., Tobo et al. 2014). Quantification of warm-temperature INPs under ambient conditions currently can only be achieved via offline methods such as the ARM ice-nucleation spectrometer (INS). Therefore, offline sampling for the INS was required to complement the real-time INP measurements conducted during the AGINSGP campaign.

, tethered balloon system↗

DOE OSTI2025

Enabling Robust Exoplanet Atmospheric Retrievals with Gaussian Processes

Atmospheric retrievals are essential tools for interpreting exoplanet transmission and eclipse spectra, enabling quantitative constraints on the chemical composition, aerosol properties, and thermal structure of planetary atmospheres. The James Webb Space Telescope (JWST) offers unprecedented spectral precision, resolution, and wavelength coverage, unlocking transformative insights into the formation, evolution, climate, and potential habitability of planetary systems. However, this opportunity is accompanied by challenges: modeling assumptions and unaccounted-for noise or signal sources can bias retrieval outcomes and their interpretation. To address these limitations, we introduce a Gaussian process (GP)-aided atmospheric retrieval framework that flexibly accounts for unmodeled features and correlated noise in exoplanet spectra. We validate this method on synthetic JWST observations, and show that GP-aided retrievals reduce bias in inferred abundances and better capture model–data mismatches than traditional approaches. We also introduce the concept of mean squared error to quantify the trade-off between bias and variance, arguing that this metric more accurately reflects retrieval performance than bias alone. We then reanalyze the NIRISS/SOSS JWST transmission spectrum of WASP-96 b, finding that GP-aided retrievals yield broader constraints on CO 2 and H 2 O, possibly alleviating tension between previous retrieval results and equilibrium predictions. Our GP framework provides precise and accurate constraints while highlighting regions where models fail to explain the data. As JWST matures and future facilities come online, a deeper understanding of the limitations of both data and models will be essential, and GP-enabled retrievals like the one presented here offer a principled path forward.

Rotman, Yoav [Arizona State Univ., Tempe, AZ (Unit↗

DOE OSTI2021

Future changes in isoprene-epoxydiol-derived secondary organic aerosol (IEPOX SOA) under the Shared Socioeconomic Pathways: the importance of physicochemical dependency

Abstract. Secondary organic aerosol (SOA) is a dominant contributor of fine particulate matter in the atmosphere, but the complexity of SOA formation chemistry hinders the accurate representation of SOA in models. Volatility-based SOA parameterizations have been adopted in many recent chemistry modeling studies and have shown a reasonable performance compared to observations. However, assumptions made in these empirical parameterizations can lead to substantial errors when applied to future climatic conditions as they do not include the mechanistic understanding of processes but are rather fitted to laboratory studies of SOA formation. This is particularly the case for SOA derived from isoprene epoxydiols (IEPOX SOA), for which we have a higher level of understanding of the fundamental processes than is currently parameterized in most models. We predict future SOA concentrations using an explicit mechanism and compare the predictions with the empirical parameterization based on the volatility basis set (VBS) approach. We then use the Community Earth System Model 2 (CESM2.1.0) with detailed isoprene chemistry and reactive uptake processes for the middle and end of the 21st century under four Shared Socioeconomic Pathways (SSPs): SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. With the explicit chemical mechanism, we find that IEPOX SOA is predicted to increase on average under all future SSP scenarios but with some variability in the results depending on regions and the scenario chosen. Isoprene emissions are the main driver of IEPOX SOA changes in the future climate, but the IEPOX SOA yield from isoprene emissions also changes by up to 50 % depending on the SSP scenario, in particular due to different sulfur emissions. We conduct sensitivity simulations with and without CO2 inhibition of isoprene emissions that is highly uncertain, which results in factor of 2 differences in the predicted IEPOX SOA global burden, especially for the high-CO2 scenarios (SSP3–7.0 and SSP5–8.5). Aerosol pH also plays a critical role in the IEPOX SOA formation rate, requiring accurate calculation of aerosol pH in chemistry models. On the other hand, isoprene SOA calculated with the VBS scheme predicts a nearly constant SOA yield from isoprene emissions across all SSP scenarios; as a result, it mostly follows isoprene emissions regardless of region and scenario. This is because the VBS scheme does not consider heterogeneous chemistry; in other words, there is no dependency on aerosol properties. The discrepancy between the explicit mechanism and VBS parameterization in this study is likely to occur for other SOA components as well, which may also have dependencies that cannot be captured by VBS parameterizations. This study highlights the need for more explicit chemistry or for parameterizations that capture the dependence on key physicochemical drivers when predicting SOA concentrations for climate studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Measurements from mobile surface vehicles during the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE)

Abstract. Between 14 and 20 July 2018, small unmanned aircraft systems (UASs) were deployed to the San Luis Valley of Colorado (USA) alongside surface-based remote sensors, in situ sensors, and radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The measurements collected as part of LAPSE-RATE targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. Additionally, intensive intercomparison between the different unmanned aircraft platforms was completed. The current paper describes the observations obtained using three different types of surface-based mobile observing vehicles. These included the University of Colorado Mobile UAS Research Collaboratory (MURC), the National Oceanic and Atmospheric Administration National Severe Storms Laboratory Mobile Mesonet, and two University of Nebraska Combined Mesonet and Tracker (CoMeT) vehicles. Over the 1-week campaign, a total of 143 h of data were collected using this combination of vehicles. The data from these coordinated activities provide detailed perspectives on the spatial variability of atmospheric state parameters (air temperature, humidity, pressure, and wind) throughout the northern half of the San Luis Valley. These datasets have been checked for quality and published to the Zenodo data archive under a specific “community” setup for LAPSE-RATE (https://zenodo.org/communities/lapse-rate/, last access: 21 January 2021) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5281/zenodo.3814765 (CU MURC measurements; de Boer et al., 2020d), https://doi.org/10.5281/zenodo.3738175 (NSSL MM measurements; Waugh, 2020), and https://doi.org/10.5281/zenodo.3838724 (UNL CoMeT measurements; Houston and Erwin, 2020).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗