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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Observation and Simulation of Methane Plumes During the Morning Boundary Layer Transition

Abstract Methane (CH 4 ) contributes significantly to global warming. However, accurate identification of CH 4 sources for reducing CH 4 emissions is often hampered by inadequate accuracy and spatiotemporal coverage of CH 4 detection, and lack of accurate CH 4 forward modeling used in top‐down inversion systems. In this study, a field experiment was conducted in Pampa, Texas using two CH 4 sensors (LI‐COR and OGI camera) to detect CH 4 releases. We investigated whether high‐resolution simulations using the Weather Research and Forecasting (WRF) model with greenhouse gases (WRF‐GHG) could accurately simulate the CH 4 plumes in the presence of evolving atmospheric boundary layer from sunrise to noon. CH 4 plumes showed substantial variation in time. At a release rate of ∼17.5 kg hr −1 , the maximum enhancement of CH 4 measured by LI‐COR was 2.6 ppm at sunrise (7:36 a.m.), 250 m from the release location. Within half an hour after sunrise, this enhancement decreased to 0.3–0.4 ppm. The enhancement was 0.2 ppm by 10:00 a.m. and further dropped to less than 0.1 ppm after 11:30 a.m. Due to the low temperature at sunrise, the OGI camera failed to detect the CH 4 plume. The WRF‐GHG large‐eddy simulation (LES) with 32 m grid spacing successfully reproduced these CH 4 enhancements. In situ measurements together with numerical simulations illustrate the impact of the transition from a stable boundary layer in the early morning to a convective boundary layer at noon on the dispersion of CH 4 plumes. Additionally, CH 4 plumes from a cattle farm in Oklahoma are briefly examined using the same modeling approach.

Hu, Xiao‐Ming [Center for Analysis and Prediction ↗
DOE OSTI2023

High-speed mapping of surface charge dynamics using sparse scanning Kelvin probe force microscopy

Unraveling local dynamic charge processes is vital for progress in diverse fields, from microelectronics to energy storage. This relies on the ability to map charge carrier motion across multiple length- and timescales and understanding how these processes interact with the inherent material heterogeneities. Towards addressing this challenge, we introduce high-speed sparse scanning Kelvin probe force microscopy, which combines sparse scanning and image reconstruction. This approach is shown to enable sub-second imaging (>3 frames per second) of nanoscale charge dynamics, representing several orders of magnitude improvement over traditional Kelvin probe force microscopy imaging rates. Bridging this improved spatiotemporal resolution with macroscale device measurements, we successfully visualize electrochemically mediated diffusion of mobile surface ions on a LaAlO 3 /SrTiO 3 planar device. Such processes are known to impact band-alignment and charge-transfer dynamics at these heterointerfaces. Furthermore, we monitor the diffusion of oxygen vacancies at the single grain level in polycrystalline TiO 2 . Through temperature-dependent measurements, we identify a charge diffusion activation energy of 0.18 eV, in good agreement with previously reported values and confirmed by DFT calculations. Together, these findings highlight the effectiveness and versatility of our method in understanding ionic charge carrier motion in microelectronics or nanoscale material systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. Further, there are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Towards real-time monitoring: data assimilated time-lapse full waveform inversion for seismic velocity and uncertainty estimation

SUMMARY Rapid development of time-lapse seismic monitoring instrumentations has made it possible to collect dense time-lapse data for tomographically retrieving time-lapse (even continuous) images of subsurface changes. While traditional time-lapse full waveform inversion (TLFWI) algorithms are designed for sparse time-lapse surveys, they lack of effective temporal constraint on time-lapse data, and, more importantly, lack of the uncertainty estimation of the TLFWI results that is critical for further interpretation. Here, we propose a new data assimilation TLFWI method, using hierarchical matrix powered extended Kalman filter (HiEKF) to quantify the image uncertainty. Compared to existing Kalman filter algorithms, HiEKF allows to store and update a data-sparse representation of the cross-covariance matrices and propagate model errors without expensive operations involving covariance matrices. Hence, HiEKF is computationally efficient and applicable to 3-D TLFWI problems. Then, we reformulate TLFWI in the framework of HiEKF (termed hereafter as TLFWI-HiEKF) to predict time-lapse images of subsurface spatiotemporal velocity changes and simultaneously quantify the uncertainty of the inverted velocity changes over time. We demonstrate the validity and applicability of TLFWI–HiEKF with two realistic CO2 monitoring models derived from Frio-II and Cranfield CO2 injection sites, respectively. In both 2-D and 3-D examples, the inverted high-resolution time-lapse velocity results clearly reveal a continuous velocity reduction due to the injection of CO2. Moreover, the accuracy of the model is increasing over time by assimilating more time-lapse data while the standard deviation is decreasing over lapsed time. We expect TLFWI-HiEKF to be equipped with real-time seismic monitoring systems for continuously imaging the distribution of subsurface gas and fluids in the future large-scale CO2 sequestration experiments and reservoir management.

58 GEOSCIENCES↗
DOE OSTI2020

Influences of North Pacific Ocean Domain Extent on the Western U.S. Winter Hydroclimatology in Variable-Resolution CESM

Variable-resolution global climate models (VRGCMs) are a dynamical downscaling method that can reach spatiotemporal scales needed for regional climate assessments. Over the years, several users of VRGCMs have assumed where the location and extent of the refinement domain should be based on knowledge of the prevailing storm tracks and resolution dependence of important regional climate processes (e.g., atmospheric rivers [ARs] and orographic uplift), but the effect of high-resolution domain size and extent on the simulation of downstream hydroclimatic phenomena has not been systematically evaluated. Here, we use variable resolution in the Community Earth System Model (VR-CESM) to perform such a test. To do this, three VR-CESM grids were generated that span the entire, two thirds, and one third of the North Pacific and evaluated for a 30-year climatology using Atmospheric Model Intercomparison Project protocols. Simulations are compared with reanalysis products offshore (fifth-generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis [ERA5]) and onshore (Livneh, 2015, https://doi.org/10.1038/sdata.2015.42, and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) of the western United States. The westward expansion of refinement domain influenced integrated vapor transport (IVT), which was generally high biased but minimally impacted AR characteristics. Due to slight differences in landfalling AR counts in the western United States, California winter precipitation generally improved with westward expansion of the refinement domain. Western U.S. mountain snowpack and surface temperatures were insensitive to refinement domain size and were more influenced by changes in topographic resolution and/or land surface model version. Here, given minimal dependence of simulated western U.S. hydroclimate on refinement domain size over the North Pacific, we advise future VR-CESM studies to focus grid resolution on better resolving land surface heterogeneity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multichannel Doppler fiber-imager spectrometer for spatiotemporal characterization of high-intensity laser-driven plasmas

Here, we demonstrate a flexible multichannel fiber-based imaging Doppler spectrometer to characterize plasmas in high intensity (≥1 × 10 18 W/cm 2 ) laser-plasma experiments at high repetition rates. This instrument collects data from ×21 different plasma locations combining optical fibers and a single imaging spectrometer. This diagnostic maps the plasma velocity evolution as a function of time with sub-pico-second resolution. Experimental results showing 2D velocity measurements of plasma with 20 μm spatial resolution are presented. Intensities of the order of 10 18 W/cm 2 were used to generate a plasma, while a much less intense, frequency doubled (400 nm), probe beam (10 11 W/cm 2 ) was used to measure the Doppler shift from the plasma critical surface. The instrument can be scaled to a larger number of channels (e.g., 100) still using a single spectrometer.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

River–aquifer interactions enhancing evapotranspiration in a semiarid riparian zone: A modelling study

The hydrologic flows across the river–aquifer interface play an important role in groundwater dynamics and biogeochemical reactions within the subsurface; however, little is known about the effects of river–aquifer interactions on land surface processes. In this study, we developed a fully coupled three-dimensional (3D) land surface and subsurface model at a high resolution (~1 km) that accounts for high-frequency hydrologic exchange flow conditions to investigate how river–aquifer interactions modulate surface water budgets in the Upper Columbia-Priest Rapids watershed, a typical semiarid watershed located in the northwestern United States where river stage fluctuates in response to reservoir releases changing. Our results show that the spatiotemporal dynamics of river–aquifer interactions are highly heterogeneous, driven mainly by river-stage fluctuations. Adding 6.64 × 10 6 m 3 year –1 of water over the watershed from the river to groundwater owing to the lateral flow, river–aquifer interactions led to an increase in soil evaporation and transpiration supplied by higher soil moisture content, particularly in deeper subsurface. In a hypothetic future scenarios where a 5-m rise in river stage was assumed, the hydrologic flow exchange rates were intensified, resulting in higher surface water over the entire watershed. Overall, lateral flow induced by river–aquifer exchanges leads to an increase in evapotranspiration of ~75% in the historical period and of ~83% in the hypothetical future scenario. Finally, our study demonstrates the potential of coupled model as an effective tool for understanding river–aquifer–land surface interactions, and indicates that river–aquifer interactions fundamentally alter the water balance of the riparian zone for the semiarid watershed and will likely become more frequent and intense in the future under the effects of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds

Abstract Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km 2 ) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Abstract Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Multi-messenger dynamic imaging of laser-driven shocks in water using a plasma wakefield accelerator

Understanding dense matter hydrodynamics is critical for predicting plasma behavior in environments relevant to laser-driven inertial confinement fusion. Traditional diagnostic sources face limitations in brightness, spatiotemporal resolution, and in their ability to detect relevant electromagnetic fields. In this work, we present a dual-probe, multi-messenger laser wakefield accelerator platform combining ultrafast X-rays and relativistic electron beams at 1 Hz, to interrogate a free-flowing water target in vacuum, heated by an intense 200 ps laser pulse. This scheme enables high-repetition-rate tracking the evolution of the interaction using both particle types. Betatron X-rays reveal a cylindrically symmetric shock compression morphology assisted by low-density vapor, resembling foam-layer-assisted fusion targets. The synchronized electron beam detects time-evolving electromagnetic fields, uncovering charge separation and ion species differentiation during plasma expansion – phenomena not captured by photons or hydrodynamic simulations. We show that combining both probes provides complementary insights spanning kinetic to hydrodynamic regimes, highlighting the need for hybrid physics models to accurately predict fusion-relevant plasma behavior.

imaging techniques↗
DOE OSTI2025

Remote Sensing Approach for Monitoring Tree Health Adjacent to Transmission Corridors

This study presents an initial proof-of-concept for a satellite-based remote sensing approach to identify and monitor potential areas of poor tree health across the entire BPA service territory on an annual basis. We tested three variants of “delta peak NDVI” ( ΔPN ) change detection metrics that express interannual variation in primary productivity relative to a baseline by comparing ΔPN values for known insect/disease disturbances and nearby reference locations. All three metrics showed promise for detecting poor tree health in the year during disturbance, but the metric based on the difference from the long-term (2016-2024) median ( Δ Med PN ) was preferred due to its responsiveness to change in the years during and after disturbance, resilience to interannual variation, and ease of interpretation as being above or below normal. Comparison of Δ Med PN grouped by relative severity of disturbance indicated it was not sensitive enough to detect “low” severity disturbances, as mapped by USGS’s LANDFIRE program, but could distinguish “moderate” and “high” severity disturbances from reference locations. These findings informed selection of a threshold for Δ Med PN , which was combined with areas exhibiting negative NDVI to map potential areas of concern. Visual inspection of before/after high-resolution imagery and NDVI time series showed that many areas of concern aligned with visible signs of defoliation and die-off as well as other types of disturbance (e.g., landslides, logging, road grading, flooding). Some areas of concern are thought to be false detections caused by persistent shadow, and some could not be explained with visual inspection due to spatiotemporal limitations of before/after imagery. In summary, our approach shows promise for large-scale monitoring of tree health adjacent to BPA transmission lines, but additional work is recommended to improve model sophistication and remove noise.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Progress Towards a Predictive Eagle Behavior and Risk Modeling Framework: Overview and Recent Validation Efforts

This presentation summarizes progress to date of the U.S. Department of Energy project, "Development of a computational framework for modeling golden eagles (Aquila chrysaetos) near wind farms," which focuses on stochastic behavioral modeling of soaring raptors across landscape, facility, and turbine spatiotemporal scales. This publicly available, open-source modeling framework includes behavioral models based on three different underlying principles: energy minimization at landscape scale, behavioral heuristics at landscape-facility scale, and data-driven behaviors at the facility-micro-scale. We will briefly overview the key advancements in the behavioral modeling state of the art, which leverages multiple high-resolution telemetry data sources combined with high-fidelity atmospheric flow modeling insights. We then present preliminary results from a validation study in Altamont, California. This new study involves a novel application of the Stochastic Soaring Raptor Simulator (SSRS), in a new geographic locale, to understand facility scale eagle movement patterns over time scales representative of a wind project's lifetime. For this desktop analysis (that does not depend on any high-performance computing resources), SSRS simultaneously considers a variety of wind conditions and eagle approach vectors toward a project site of interest. This work demonstrates the integration of publicly available landscape-scale atmospheric datasets, our recently improved engineering updraft models (see presentation from Thedin et al.), and our energy minimization behavioral models within the SSRS framework. While we only present results from a single behavioral model, the integration of these three modeling components forms the foundation for our more sophisticated behavioral models (see presentations from Brandes et al., Sandhu et al.) that are under active development. Results are presented in the form of presence maps, which may be applied to estimate risk to wildlife, augment ground survey data, inform wind-plant operations, or incorporated into wind-plant designs.

agent-based modeling↗
DOE OSTI2026

Spatio-Kinetic Electronic Heterogeneity in MoS 2 Revealed by Ultrafast Electron Microscopy: Implications for Optoelectronics

There is broad interest in layered, two-dimensional (2D) semiconductors owing to their high optical absorption and ability to heterogeneously integrate with various substrates. In such layered semiconductors, mesoscopic imperfections, such as surface step edges, may affect charge carrier kinetics and device performance, but studying their effects on carrier transport with sufficient spatial and temporal resolution is a challenge. Using scanning ultrafast electron microscopy, we image the near-surface charge carrier kinetics with μm/ps spatiotemporal resolution in single-crystal MoS 2 samples with surface steps tens of nanometers in height. We find an approximately 3-fold increase in surface trap density near such surface steps compared to the uniform, flat surface regions. Despite the difference in trap state density, filling of the traps occurs over the same time scale (∼100 ps), but carrier recovery is much slower near the step compared to the uniform regions of the material. Furthermore, these results provide an approach to measure ultrafast dynamics and determine the required uniformity of layered materials depending on the speed limit constraints for specific applications.

SEM↗
DOE OSTI2025

Turbo‐charging crop improvement: harnessing multiplex editing for polygenic trait engineering and beyond

Multiplex CRISPR editing has emerged as a transformative platform for plant genome engineering, enabling the simultaneous targeting of multiple genes, regulatory elements, or chromosomal regions. This approach is effective for dissecting gene family functions, addressing genetic redundancy, engineering polygenic traits, and accelerating trait stacking and de novo domestication. Its applications now extend beyond standard gene knockouts to include epigenetic and transcriptional regulation, chromosomal engineering, and transgene‐free editing. These capabilities are advancing crop improvement not only in annual species but also in more complex systems such as polyploids, undomesticated wild relatives, and species with long generation times. At the same time, multiplex editing presents technical challenges, including complex construct design and the need for robust, scalable mutation detection. We discuss current toolkits and recent innovations in vector architecture, such as promoter and scaffold engineering, that streamline workflows and enhance editing efficiency. High‐throughput sequencing technologies, including long‐read platforms, are improving the resolution of complex editing outcomes such as structural rearrangements—often missed by standard genotyping—when targeting repetitive or tandemly spaced loci. To fully realize the potential of multiplex genome engineering, there is growing demand for user‐friendly, synthetic biology‐compatible, and scalable computational workflows for gRNA design, construct assembly, and mutation analysis. Experimentally validated inducible or tissue‐specific promoters are also highly desirable for achieving spatiotemporal control. As these tools continue to evolve, multiplex CRISPR editing is poised to become a foundational technology of next‐generation crop improvement to address challenges in agriculture, sustainability, and climate resilience.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Scaling Arctic landscape and permafrost features improves active layer depth modeling

Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Final technical report for DE-SC0022255: Discovering Physically Meaningful Structures from Climate Extreme Data

The past two decades have witnessed natural disasters and extreme weather events that affect millions of people. At the same time, the data volume from high-resolution climate models, satellite, in-situ and ground-based measurements have substantially increased to petabyte scales. These new and readily accessible datasets create the previously missing pipeline required for scientific machine learning (ML) and therefore new opportunities for improved understanding and prediction capability of climate extreme events. This project developed a deep latent variable model framework to discover physically meaningful hidden structures from high-dimensional, spatiotemporal climate extreme data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Understanding processes that control dust spatial distributions with global climate models and satellite observations

Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the Northern Hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the Southern Hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Influence of the Kuroshio Intrusion on Deep Flow Intraseasonal Variability in the Northern South China Sea

Abstract Interactions between the open ocean and marginal seas have been suggested to be critical to the redistribution and dissipation of global energy. Here, we propose a mechanism for the upper open ocean influencing the deep flow in marginal seas that hinges on the formation and propagation of topographic Rossby waves (TRWs). Observations and high‐resolution simulations suggest substantial intraseasonal variability with periods of 5–60 days associated with the deep flow over continental slopes of the northern South China Sea (NSCS). These fluctuations generally account for over 40% of the total deep kinetic energy variability and the number can reach 70% over the slopes to the west of the Luzon Strait, southwest of the Dongsha Islands, and northeast of the Xisha Islands. By examining the spatiotemporal features of the fluctuations, we demonstrate that the intraseasonal variability of the deep flow in the NSCS is closely associated with TRWs. Using a recently developed multiscale energetics analysis in combination with a ray tracing model, we show that the energy sources of TRWs can be traced back to the east of the Dongsha Islands, where the Kuroshio intrusion and related eddies energize the TRWs primarily through pressure work. These waves propagate westward across the NSCS and drive the intraseasonal variability of the deep flow over continental slopes. Our findings highlight an energy pathway from the open ocean western boundary current to the abyssal marginal sea that could modulate regional circulation as well as exchanges between major ocean basins.

Quan, Qi↗