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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Synoptic Drivers of Atmospheric River Induced Precipitation Near Dronning Maud Land, Antarctica

Atmospheric rivers (ARs) that reach the Antarctic Ice Sheet (AIS) transport anomalous moisture from lower latitudes and can impact the AIS via extreme precipitation and increased downward longwave radiation. ARs contribute significantly to the interannual variability of precipitation over the AIS and thus are likely to play a key role in understanding future changes in the surface mass balance of the AIS. Dronning Maud Land (DML) is one of four maxima in AR frequency over coastal East Antarctica, with AR precipitation explaining 77% of the interannual variability in precipitation for this region. We employ a 16-node self-organizing map (SOM) trained with MERRA-2 sea-level pressure anomalies to identify synoptic-scale environments associated with landfalling ARs in and around DML. Node composites of atmospheric variables reveal common drivers of precipitation associated with ARs reaching DML including anomalous high-low surface pressure couplets, anomalously high integrated water vapor, and coastal barrier jets. Using a quasi-geostrophic framework, we find that upward vertical motion associated with the occlusion process of attendant cyclones dominates atmospheric lift in AR environments. We further identify mechanisms that explain the variability in AR precipitation intensity across nodes, such as the lift associated with the occlusion process of attendant cyclones and the spatial coincidence of ascent induced by the occlusion process and frontogenesis. Here, the latter suggests that ARs making landfall during the mature phase of cyclogenesis result in higher precipitation intensity compared to landfalling ARs that occur during the occluded phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Subseasonal Clustering of Atmospheric Rivers Over the Western United States

Abstract The serial occurrence of atmospheric rivers (ARs) along the US West Coast can lead to prolonged and exacerbated hydrologic impacts, threatening flood‐control and water‐supply infrastructure due to soil saturation and diminished recovery time between storms. Here a statistical approach for quantifying subseasonal temporal clustering among extreme events is applied to a 41‐year (1979–2019) wintertime AR catalog across the western United States (US). Observed AR occurrence, compared against a randomly distributed AR timeseries with the same average event density, reveals temporal clustering at a greater‐than‐random rate across the western US with a distinct geographical pattern. Compared to the Pacific Northwest, significant AR clusters over the northern Coastal Range of California and Sierra Nevada are more frequent and occur over longer time periods. Clusters along the California Coastal Range typically persist for 2 weeks, are composed of 4–5 ARs per cluster, and account for over 85% of total AR occurrence. Across the northwest Coast‐Cascade Ranges, clusters account for ∼50% of total AR occurrence, typically last 8–10 days, and contain 3–4 individual AR events. Based on precipitation data from a high‐resolution dynamical downscaling of reanalysis, the fractions of total and extreme hourly precipitation attributable to AR clusters are largest along the northern California coast and in the Sierra Nevada. Interannual variability among clusters highlights their importance for determining whether a particular water year is anomalously wet or dry. The mechanisms behind this unusual clustering are unclear and require further research.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Modeling impacts of ice-nucleating particles from marine aerosols on mixed-phase orographic clouds during 2015 ACAPEX field campaign

Abstract. A large fraction of annual precipitation over the western United States comes from wintertime orographic clouds associated with atmospheric rivers (ARs). Transported African and Asian dust and marine aerosols from the Pacific Ocean may act as ice-nucleating particles (INPs) to affect cloud and precipitation properties over the region. Here we explored the effects of INPs from marine aerosols on orographic mixed-phase clouds and precipitation at different AR stages for an AR event observed during the 2015 ACAPEX field campaign under low dust (<0.02 cm−3) conditions. Simulations were conducted using the chemistry version of the Weather Research and Forecasting Model coupled with the spectral-bin microphysics at 1 km grid spacing, with ice nucleation connected with dust and marine aerosols. By comparing against airborne and ground-based observations, accounting for marine INP effects improves the simulation of AR-precipitation. The marine INPs enhance the formation of ice and snow, leading to less shallow warm clouds but more mixed-phase and deep clouds, as well as to a large spillover effect of precipitation after AR landfall. The responses of cloud and precipitation to marine INPs vary with the AR stages, with more significant effects before AR landfall and post-AR than after AR landfall, mainly because the moisture and temperature conditions change with the AR evolution. This work suggests weather and climate models need to consider the impacts of marine INPs since their contribution is notable under low dust conditions despite the much lower relative ice nucleation efficiency of marine INPs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Shifting Scales of Western U.S. Landfalling Atmospheric Rivers Under Climate Change

Atmospheric rivers (ARs) can be a boon and bane to water resource managers as they have the ability to replenish water reserves, but they can also generate million-to-billion-dollar flood damages. To investigate how anthropogenic climate change may influence AR characteristics in the coastal western United States by end century, we employ a suite of novel tools such as variable resolution in the Community Earth System Model (VR-CESM), the TempestExtremes AR detection algorithm, and the Ralph, Rutz, et al. (2019, https://doi.org/10.1175/BAMS-D-18-0023.1) AR category scale.We show that end-century ARs primarily shift from being “mostly or primarily beneficial” to “mostly or primarily hazardous” with a concomitant sharpening and intensification of winter season precipitation totals. Changes in precipitation totals are due to a significant increase in AR (+260%) rather than non-AR (+7%) precipitation, largely through increases in the most intense category of AR events and a decrease in the interval between landfalling ARs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Atmospheric Rivers in the Eastern and Midwestern United States Associated With Baroclinic Waves

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic-scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long-wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

58 GEOSCIENCES↗
DOE OSTI2023

Structural basis of agonist specificity of α 1A -adrenergic receptor

α 1 -adrenergic receptors (α 1 -ARs) play critical roles in the cardiovascular and nervous systems where they regulate blood pressure, cognition, and metabolism. However, the lack of specific agonists for all α 1 subtypes has limited our understanding of the physiological roles of different α 1 -AR subtypes, and led to the stagnancy in agonist-based drug development for these receptors. Here we report cryo-EM structures of α 1A -AR in complex with heterotrimeric G-proteins and either the endogenous common agonist epinephrine or the α 1A -AR-specific synthetic agonist A61603. These structures provide molecular insights into the mechanisms underlying the discrimination between α 1A -AR and α 1B -AR by A61603. Guided by the structures and corresponding molecular dynamics simulations, we engineer α 1A -AR mutants that are not responsive to A61603, and α 1B -AR mutants that can be potently activated by A61603. Together, these findings advance our understanding of the agonist specificity for α 1 -ARs at the molecular level, opening the possibility of rational design of subtype-specific agonists.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models

We present a new atmospheric river (AR) analysis and benchmarking tool, namely Atmospheric River Metrics Package (ARMP). It includes a suite of new AR metrics that are designed for quick analysis of AR characteristics via statistics in gridded climate datasets such as model output and reanalysis. This package can be used for climate model evaluation in comparison with reanalysis and observational products. Integrated metrics such as mean bias and spatial pattern correlation are efficient for diagnosing systematic AR biases in climate models. For example, the package identifies the fact that, in CMIP5 and CMIP6 (Coupled Model Intercomparison Project Phases 5 and 6) models, AR tracks in the South Atlantic are positioned farther poleward compared to ERA5 reanalysis, while in the South Pacific, tracks are generally biased towards the Equator. For the landfalling AR peak season, we find that most climate models simulate a completely opposite seasonal cycle over western Africa. This tool can also be used for identifying and characterizing structural differences among different AR detectors (ARDTs). For example, ARs detected with the Mundhenk algorithm exhibit systematically larger size, width, and length compared to the TempestExtremes (TE) method. The AR metrics developed from this work can be routinely applied for model benchmarking and during the development cycle to trace performance evolution across model versions or generations and set objective targets for the improvement of models. They can also be used by operational centers to perform near-real-time climate and extreme event impact assessments as part of their forecast cycle.

58 GEOSCIENCES↗
DOE OSTI2023

Climatology and surface impacts of atmospheric rivers on West Antarctica

Abstract. Atmospheric rivers (ARs) transport large amounts of moisture from the mid- to high-latitudes and they are a primary driver of the most extreme snowfall events, along with surface melting, in Antarctica. In this study, we characterize the climatology and surface impacts of ARs on West Antarctica, focusing on the Amundsen Sea Embayment and Marie Byrd Land. First, we develop a climatology of ARs in this region, using an Antarctic-specific AR detection tool combined with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) atmospheric reanalyses. We find that while ARs are infrequent (occurring 3 % of the time), they cause intense precipitation in short periods of time and account for 11 % of the annual surface accumulation. They are driven by the coupling of a blocking high over the Antarctic Peninsula with a low-pressure system known as the Amundsen Sea Low. Next, we use observations from automatic weather stations on Thwaites Eastern Ice Shelf with the firn model SNOWPACK and interferometric reflectometry (IR) to examine a case study of three ARs that made landfall in rapid succession from 2 to 8 February 2020, known as an AR family event. While accumulation dominates the surface impacts of the event on Thwaites Eastern Ice Shelf (> 100 kg m−2 or millimeters water equivalent), we find small amounts of surface melt as well (< 5 kg m−2). The results presented here enable us to quantify the past impacts of ARs on West Antarctica's surface mass balance (SMB) and characterize their interannual variability and trends, enabling a better assessment of future AR-driven changes in the SMB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Augmentation of progestin signaling rescues testis organization and spermatogenesis in zebrafish with the depletion of androgen signaling

Disruption of androgen signaling is known to cause testicular malformation and defective spermatogenesis in zebrafish. However, knockout of cyp17a1 , a key enzyme responsible for the androgen synthesis, in ar -/- male zebrafish paradoxically causes testicular hypertrophy and enhanced spermatogenesis. Because Cyp17a1 plays key roles in hydroxylation of pregnenolone and progesterone (P4), and converts 17α-hydroxypregnenolone to dehydroepiandrosterone and 17α-hydroxyprogesterone to androstenedione, we hypothesize that the unexpected phenotype in cyp17a1 -/-; androgen receptor ( ar )-/- zebrafish may be mediated through an augmentation of progestin/nuclear progestin receptor (nPgr) signaling. In support of this hypothesis, we show that knockout of cyp17a1 leads to accumulation of 17α,20β-dihydroxy-4-pregnen-3-one (DHP) and P4. Further, administration of progestin, a synthetic DHP mimetic, is sufficient to rescue testicular development and spermatogenesis in ar -/- zebrafish, whereas knockout of npgr abolishes the rescue effect of cyp17a1 -/- in the cyp17a1 -/-; ar -/- double mutant. Analyses of the transcriptomes among the mutants with defective testicular organization and spermatogenesis ( ar -/-, ar -/-; npgr -/- and cyp17a -/-; ar -/-; npgr -/-), those with normal phenotype (control and cyp17a1 -/-), and rescued phenotype ( cyp17a1 -/-; ar -/-) reveal a common link between a downregulated expression of insl3 and its related downstream genes in cyp17a -/-; ar -/-; npgr -/- zebrafish. Taken together, our data suggest that genetic or pharmacological augmentation of the progestin/nPgr pathway is sufficient to restore testis organization and spermatogenesis in zebrafish with the depletion of androgen signaling.

Zhai, Gang↗
DOE OSTI2024

Quantifying the Potential of Argon Detection Capabilities for Nuclear Explosion Monitoring

Abstract Current noble gas detection systems for nuclear explosion monitoring are based on the detection of four radioxenon isotopes—Xe-131m, -133, -133m and -135. The data provided by radioxenon detection could be enhanced by other radionuclide signatures such as Ar-37. Activation of Ca-40 in rock by neutrons produces Ar-37, and monitoring for this additional nuclide could help distinguish detections of nuclear explosions from background sources, such as medical isotope production. This work studies the capabilities of a hypothetical argon detection network. A 10 kt explosion was modeled using MCNP and SCALE to determine the inventory of Ar-37 created in a representative granite rock layer, assuming either 0.1, 1 or 10% of the total inventory was released. The Ar-37 inventory was combined with atmospheric transport data from HYSPLIT compiled in a previous study, along with the detection limits of standard Ar-37 detection systems, to determine how many hypothetical monitoring stations would detect Ar-37 from an explosion. This method was repeated for 365 HYSPLIT data sets to create a year’s worth of hypothetical explosions, releases, and detections. The study quantified the average number of detections per release, the number of stations detecting Ar-37, and the possibility of detecting Ar-37 in coincidence with xenon.

37Ar↗
DOE OSTI2023

Noble gas insights into early impact delivery and volcanic outgassing to Earth's atmosphere: A limited role for the continental crust

Earth's atmosphere, crust and mantle have evolved together through continuous geochemical exchange throughout Earth's history. Constraints on the transport of volatile elements and compounds between these reservoirs are crucial for understanding how Earth could have stayed habitable for extended periods of time. In this work, we present a new forward model of He, Ne and Ar in the mantle, crust and atmosphere. We explore concentrations of noble gases at the end of accretion, bulk silicate Earth K/U ratios, crustal growth scenarios, and upper mantle processing rates throughout Earth's history. We search for parameter combinations that simultaneously satisfy observational constraints on present-day mantle 4 He/ 3 He and 40 Ar/ 36 Ar (sensitive to mantle outgassing and continental crust growth, which depletes the mantle of U, Th and K), atmospheric 20 Ne/ 22 Ne (which tracks the mix of outgassed vs. delivered Ne in the atmosphere), and atmospheric 40 Ar/ 36 Ar in the past and today (sensitive to volatile delivery, mantle outgassing and crustal growth). Leveraging this intertwined set of noble gas abundances and isotopic compositions yields new constraints on initial noble gas abundances in the mantle and on the proportions of atmospheric volatiles originating from delivery by impact degassing, mantle outgassing, and degassing of the continental crust. We find that atmospheric Ar isotopic evolution is primarily sensitive to the mantle processing rate history; the atmospheric Ar isotopic record should therefore not be used to reconstruct continental crust growth, but instead provides valuable insights into mantle processing rates. Our model predicts a measurably low 20 Ne/ 22 Ne ratio of ~9.7 in Archean atmospheric samples. Most of atmospheric primordial 36 Ar was directly delivered by chondritic bodies and not transferred to the atmosphere during an intense early episode of mantle outgassing. Nitrogen delivered by impact degassing could account for the present-day atmospheric nitrogen inventory.

58 GEOSCIENCES↗
DOE OSTI2022

Contrasting Climatic Trends of Atmospheric River Occurrences Over East Asia

Atmospheric rivers (ARs) are increasingly recognized as a major driver of hydrological cycles, and are projected to increase around the world under a changing climate. However, the long-term trends of ARs over East Asia (EA) remains less elucidated. Here we fill the gap by developing a longest-ever archive of EA ARs, and examine its role in determining spatiotemporal precipitation variability over EA. We find contrasting changes in AR occurrences, with more frequent ARs in low latitudes but less in high latitudes during the period 1950-2020. The “dipole” pattern of decadal changes in AR occurrences is dictated by atmospheric dynamics (i.e., winds) in the north but thermodynamics (i.e., moisture) in the south. The reduced AR occurrences explain 49% of decreased annual precipitation in northern China, while more AR-related precipitation is observed in southern China. Furthermore, our results provide new insights into regional hydroclimate over EA by connecting it with large-scale weather systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Contribution of Subtropical Moisture Within an Atmospheric River on Moisture Flux, Cloud Structure, and Precipitation Over the Salmon River Mountains of Idaho Using Moisture Tracers

The impact of an atmospheric river (AR) on the flux of subtropical moisture across Idaho's Salmon River Mountains and precipitation over the mountains is evaluated using the Weather, Research, and Forecasting model with water vapor tracers (WRF-WVT). The AR impacted Idaho between 17 and 19 January 2017 during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) campaign. WRF-WVT is configured to isolate the subtropical moisture contribution to the AR, the moisture flux, and precipitation. Subtropical water vapor advected by the AR into Idaho is tagged and tracked in three-dimensional space throughout the run. This allows the contribution of the subtropical moisture to the vertical distribution of water vapor and the precipitation to be directly calculated. The simulated cloud structure is compared with airborne radar data collected during two SNOWIE intensive operation periods. This study found that more than 70% of the moisture flux and more than 80% of the precipitation across the Idaho Mountains during SNOWIE IOP 4 could be attributed to subtropical moisture within the AR. Nearly all of the moisture flux in the upper cloud and 50% of the moisture in the lower cloud was attributable to the subtropical moisture. The subtropical moisture contribution within the AR to precipitation ranged from 35% in northern Idaho to more than 90% in southern Idaho. Across the entire period of impact of the AR, more than 60% of precipitation in Idaho was attributable to the subtropical moisture within the AR, with this percentage increasing toward the south across the state.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improved Simulations of Atmospheric River Climatology and Variability in High-Resolution CESM

Atmospheric rivers (ARs) can trigger extreme rainfall and snowfall events, causing high risk of flooding. Meanwhile, they are sometimes responsible for the occurrence and break of drought events. So, it is of great importance for weather and climate forecast models to accurately represent ARs. Studies have shown that AR variability is linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, Pacific-North America pattern, providing the potential for predicting ARs at subseasonal-to-decadal timescales. In this study, we examine the impact of climate model resolution on AR simulations and their relationship with major climate modes by comparing a pair of high-resolution and a low-resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results reveal major deficiencies in LR that include underestimated climatological AR strength and associated precipitation, and inaccurate representations of ARs relationships with climate modes. Significant improvements are observed by increasing CESM horizontal resolution, suggesting a path for more accurate prediction of ARs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evaluating Atmospheric River Impacts on Energy and Moisture Transport in the Arctic Using Different Detection Algorithms

Atmospheric rivers (ARs) significantly impact the Arctic climate system by enhancing atmospheric heat and moisture transport and altering the local energy budget. Developing AR detection tools (ARDTs) is critical yet challenging. This study evaluates 12 ARDTs in the Arctic to assess their performance in representing atmospheric heat (represented by moist static energy) and moisture transport, as well as surface downward longwave radiation (LWD) and precipitation impacts, spanning 2000 to 2019 using ERA5 reanalysis. We find that AR occurrence frequency in the Arctic varies widely, from less than 1% to over 13%, depending on the ARDT. This variability leads to differences in contributions to poleward atmospheric heat (<1%–33%) and moisture (<1%–49%) transport. The highest AR frequency, and corresponding contributions to atmospheric heat and moisture transport, occurs over the Atlantic sector during non-summer seasons for most ARDTs. This region aligns with the primary poleward moisture pathway and the end of climatological mid-latitude storm tracks, highlighting strong connections between Arctic ARs and mid-latitude cyclones. ARs induce significant LWD anomalies, largest in winter, smallest in summer, and also substantially contribute to the seasonal precipitation. Global ARDTs detect fewer ARs with larger anomalies (>100 W m−2 in higher Arctic), but contribute <1% to seasonal climatological LWD and precipitation. In contrast, polar-specific ARDTs detect higher AR occurrences and account for up to 16% of seasonal LWD and 41% precipitation. This suggests that algorithms emphasizing extreme events with large anomalies do not necessarily indicate a large climate radiative and precipitation impact.

Zhang, Chen [University of Colorado, Boulder, CO (↗
DOE OSTI2025

Response of Atmospheric River Width and Intensity to Aquaplanet Warming: A Detection Algorithm‐ and Background Moisture‐Independent Approach

The width of an atmospheric river (AR) is an important parameter when evaluating its impact. Although previous research suggests ARs will widen with global warming, a precise response has been muddled by the large sensitivity of width to a diverse set of AR detection techniques (ARDTs). Here, we propose a methodology that removes the influence of the ARDT by modeling AR‐integrated vapor transport (IVT) profiles as idealized exponential curves with free parameters given by background IVT, intensity above background IVT, and profile width. Notably, our definition for AR profile width does not include any explicit numerical thresholds, relative or absolute, for IVT. We apply our approach to a series of idealized aquaplanet experiments, first with a baseline sea surface temperature (SST) distribution, and then with +2K, +4K, and +6K uniform warming, so as to determine the contributions of each free parameter to AR width. We also apply our approach to high‐resolution atmosphere‐only models forced with SSTs modified to preserve historical variability but following projected warming over 2016–2050. Our results show that contributions to impacts‐relevant AR widening comes primarily from enhancements in background IVT and AR intensity, as opposed to from dynamic width changes.

54 ENVIRONMENTAL SCIENCES↗