Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “global models”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

GLAD-M35: a joint P and S global tomographic model with uncertainty quantification

We present our third and final generation joint P and S global adjoint tomography (GLAD) model, GLAD-M35, and quantify its uncertainty based on a low-rank approximation of the inverse Hessian. Starting from our second-generation model, GLAD-M25, we added 680 new earthquakes to the database for a total of 2160 events. New P-wave categories are included to compensate for the imbalance between P- and S-wave measurements, and we enhanced the window selection algorithm to include more major-arc phases, providing better constraints on the structure of the deep mantle and more than doubling the number of measurement windows to 40 million. Two stages of a Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton inversion were performed, each comprising five iterations. With this BFGS update history, we determine the model’s standard deviation and resolution length through randomized singular value decomposition.

58 GEOSCIENCES↗
DOE OSTI2024

Ensemble modeling of global lake evaporation under climate change

Global projections of lake evaporation are typically based on simulations using single mechanistic models. However, because of its complex interactions with various lake physical properties, environmental and anthropogenic drivers, lake evaporation is highly variable and sensitive to the choice of model used. In this study, we present a multi-model analysis to investigate differences across global simulations of lake evaporation during the warm-season using three different lake models driven by outputs from four general circulation models (GCM) (i.e. 12 model combinations in total) for historic and future scenarios. Our results suggest substantial differences among lake-climate model simulations of lake evaporation. These differences varied throughout the 20th and 21st century, with model driver data explaining 74% of the variance in future projections of warm- season lake evaporation. Our projections indicate that, by the end of the 21st century (2070–2099), global annual lake evaporation rates will increase by 10–27% under Representative Concentration Pathways (RCPs) 2.6–8.5. Finally, we highlight the importance of using a multi-model approach for the prediction of future global lake evaporation responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effects of Urbanization in China on the East Asian Summer Monsoon as Revealed by Two Global Climate Models

Abstract The East Asian summer monsoon (EASM), vulnerable to anthropogenic activities, exerts profound impacts on precipitation in East Asia. In recent decades, China has been experiencing rapid urbanization. It has not yet reached a consensus on its influences on EASM. Here, we introduce satellite‐derived urban impervious areas of China during 1985–2017 into two global climate models with contrasting biases of climatological EASM simulation. Both models consistently show that urbanization weakens EASM by increasing surface friction drag. The increased surface roughness reduces climatological southerly winds in the concentrated urbanization area. As a result, moisture convergence occurs south of the concentrated urbanization area with ascending motion, while moisture divergence with descending motion appears to the north, resulting in the precipitation anomalies of “southern flood and northern drought.” The dominant role of urban dynamic forcing in regulating EASM is confirmed by reducing urban roughness to the level of croplands in sensitivity experiments. As urban dynamic forcing is largely mitigated, the urbanization impact mainly driven by urban thermal forcing conversely strengthens EASM due to enhanced land‐sea thermal contrast via urbanization‐induced warming. Spatial patterns and magnitudes of the urbanization‐induced EASM weakening vary between the two models due to disparities in their simulations of urban thermodynamic forcing and atmospheric adjustments. These results underscore the robust weakening effect of urbanization on EASM by urban friction drag and document uncertainties in the related spatial pattern and magnitude resulting from model discrepancies.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Decadal Variability of Southeast US Rainfall in an Eddying Global Coupled Model

Abstract Ocean variability is a dominant source of remote rainfall predictability, but in many cases the physical mechanisms driving this predictability are not fully understood. This study examines how ocean mesoscales (i.e., the Gulf Stream SST front) affect decadal Southeast US (SEUS) rainfall, arguing that the local imprint of large‐scale teleconnections is sensitive to resolved mesoscale features. Based on global coupled model experiments with eddying and eddy‐parameterizing ocean, we find that a resolved Gulf Stream improves localized rainfall and remote circulation response in the SEUS. The eddying model generally improves the air‐sea interactions in the Gulf Stream and the North Atlantic Subtropical High that modulate SEUS rainfall over decadal timescales. The eddy‐parameterizing simulation fails to capture the sharp SST gradient associated with the Gulf Stream and overestimates the role of tropical Pacific SST anomalies in the SEUS rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Benchmark of the KGMf with a coupled Boltzmann equation solver

The Kinetic Global Model framework (KGMf) is an open-source general-purpose global model (spatially averaged) simulation code developed to explore the reaction kinetics and pathways in plasma discharge systems. It contains species continuity and electron energy balance equations, with a time-dependent evaluated electron energy distribution function (EEDF) for electron impact reactions. The EEDF is utilized to determine the rate coefficients for electron impact reactions, which can have profound impact on the temporal evolutions of plasma parameters. Previously, the EEDF was commonly assumed as Maxwellian or an analytical function of the effective electron temperature. In this work, the KGMf is coupled with a Boltzmann equation (BE) solver to self-consistently compute the EEDF. The EEDF evolution frequency is determined based on relative changes of the reduced electric field. The KGMf is benchmarked with the ZDPlasKin code based on high-pressure low-temperature argon plasma discharge cases. Additionally, the temporal evolutions of reduced electric field, electron temperature, EEDF, reaction rates, and species densities, are obtained and compared under different discharge conditions, showing good agreement between the KGMf and the ZDPlasKin simulations. The application of the KGMf for predicting breakdown times in high power microwave discharges is also presented, which shows qualitative agreement with particle-in-cell simulations. The KGMf can be further applied for more complicated plasma discharge systems, where the reaction kinetics are more intricate (e.g., plasma-assisted combustion systems).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Integrated Model of Models for Global Flood Alerting

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures, specifically, by providing situational awareness in near real-time about flood impacts to enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions to forecast flood events at regional to global scale. Given the global coverage of two operational flood models – GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the purpose of this project is to implement a Model of Models (MoM) approach to integrate the outputs from these two models to classify flood severity at watershed level worldwide, and send alerts based on severity similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes) to flood impacted communities. The alerts containing flood impacts and severity information will be disseminated through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. The current version of the MoM approach was implemented for a case study flood event that occurred during January and February of 2020 in South and Central Africa. The findings of the case study event reveal that the approach is effective in identifying potential flood impact areas and the spatio-temporal variation of flood severity, flood depth and extent at watershed level, which will be used to assess infrastructure and societal impacts using earth-observation data and for alerting.

Kar, Bandana↗
DOE OSTI2021

Global maximum carboxylation rate trait scaling in the Sheffield Dynamic Global Vegetation Model 1901-2012

This data package contains model output data and climate input data associated with the New Phytologist publication: Walker, A.P., Quaife, T., van Bodegom, P.M., De Kauwe, M.G., Keenan, T.F., Joiner, J., Lomas, M.R., MacBean, N., Xu, C., Yang, X., Woodward, F.I., 2017. The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (Vcmax) on global gross primary production. New Phytologist 215, 1370–1386. https://doi.org/10.1111/nph.14623. This dataset contains maximum carboxylation rate at 25 ºC (Vcmax,25), Gross Primary Production (GPP), Net Biome Production (NBP), vegetation carbon, and soil carbon data from Sheffield Dynamic Global Vegetation Model (SDGVM) for multiple trait-scaling methods for scaling Vcmax,25 across Earth, and alternative instantaneous temperature scaling functions. Vcmax,25 values are mean top-leaf values taken during the growing seasons, which were defined as periods during which leaf area index (LAI) was greater than one. Vcmax,25 values were reported prior to scaling of Vcmax by water-stress or leaf-age. The attached metadata file (SDGVM_vcmax_dataset.docx) describes the methods used and explains the organization of data within the included .zip file. This metadata file is also included in PDF format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks

Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models can only take us so far, as they require significant computational resources, especially when attempting to characterize extreme events, which are rare and thus demand long and numerous simulations in order to accurately represent their changing statistics. Here we use deep learning in a proof of concept that lays the foundation for emulating global climate model output for different scenarios. We train two "loosely conditioned" Generative Adversarial Networks (GANs) that emulate daily precipitation output from a fully coupled Earth system model: one GAN modeling Fall-Winter behavior and the other Spring-Summer. Our GANs are trained to produce spatiotemporal samples: 32 days of precipitation over a 64x128 regular grid discretizing the globe. We evaluate the generator with a set of related performance metrics based upon KL divergence, and find the generated samples to be nearly as well matched to the test data as the validation data is to test. We also find the generated samples to accurately estimate the mean number of dry days and mean longest dry spell in the 32 day samples. Our trained GANs can rapidly generate numerous realizations at a vastly reduced computational expense, compared to large ensembles of climate models, which greatly aids in estimating the statistics of extreme events.

climate emulation, extreme climate, impacts, machi↗
DOE OSTI2025

Exposing Process‐Level Biases in a Global Cloud Permitting Model With ARM Observations

The emergence of global convective‐permitting models (GCPMs) represents a significant advancement in climate modeling, offering improved representation of deep convection and complex precipitation patterns. In this study, we evaluate the performance of the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) using its doubly periodic configuration (DP‐SCREAM) against large eddy simulations and modern observational data sets from the Atmospheric Radiation Measurement program. We introduce several new transitional cloud regime cases, such as the transition from shallow to deep convection and from stratocumulus to cumulus, as well as cold‐air outbreak scenarios. The results reveal both strengths and limitations of SCREAM, particularly in the accurate simulation of cloud transitions and midlevel convection, with varying degrees of sensitivity to horizontal and vertical resolution. Despite improvements at higher resolutions, key biases remain, including the abrupt transition from shallow to deep convection and the lack of congestus clouds. These findings underscore the need for further refinement in turbulence parameterizations and vertical grid resolution in GCPMs.

Bogenschutz, Peter A. [Lawrence Livermore National↗
DOE OSTI2022

Mesoscale Convective Systems Simulated by a High–Resolution Global Nonhydrostatic Model Over the United States and China

Mesoscale convective systems (MCSs) contribute a large fraction of warm-season precipitation and generate hazardous weather with substantial socio-economic impacts. Uncertainties in convection parameterizations in climate models limit our understanding of MCS characteristics and reliability of future projection. We examine the MCS simulation from the global 14-km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) without cumulus parameterization against satellite observation from Global Precipitation Measurement (GPM) during 2001-2008. We focus on MCSs over the central U.S. and eastern China where MCSs prevail from March to August. A process-oriented tracking method incorporating both cloud and precipitation criteria is used to identify and track MCSs. About 140/100 MCSs initiate in the central U.S./eastern China per warm season and most of them initiate east of high mountains and in coastal regions. The frequency distribution of MCS lifetime is well captured in NICAM. But the simulated MCSs have stronger precipitation, smaller precipitation area, and larger cold cloud system than observed in both regions, which may be caused by weak entrainment as it is not well resolved at 14 km resolution. Here, the simulated MCS number is also underestimated in summer. By examining the climatological and MCS large-scale environments, the significant underestimation of MCS number in summer over the central U.S. may be attributed to the large climatological dry bias in the atmosphere. For China, mean moisture in summer is well simulated but deficiency in capturing the dynamic condition related to the coastal topography for triggering convection may have contributed to underestimation of MCS even in a sufficiently moist environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Genomes to ecosystem function: Targeting critical knowledge gaps in methanogenesis and translation to updated global biogeochemical models

Natural freshwater temperate wetland systems currently represent the largest natural source of atmospheric methane, but are relatively understudied using systems biology tools (e.g. meta-omics) compared to other high producing methane systems (e.g. peat, tropical, or reconstructed wetlands). Using field investigations at the NOAA operated sentinel site on Lake Erie, methane producing activities and responses to geochemical conditions will be determined along seasonal and spatial gradients (Objective 1). Here, a combination of high-throughput activity and gas measurements, combined with high-resolution systems biology and analytical methods, will provide in depth knowledge of the microbiological, chemical, and physical constraints on methane production in wetlands. Using laboratory microcosms, the formation of anoxic microsites and their capacity to facilitate methane production in wetland soils will be simulated (Objective 2). This objective will validate the findings from the field investigations, offering a more controlled environment for teasing out the role of different yet interrelated variables. Lastly, these field and laboratory data will be used for multi-scale, process-level evaluation of an ecosystem biogeochemical model that accommodates these newly identified processes and parameterizes representation of these processes along relevant environmental gradients (Objective 3).This research will identify multiple interacting geochemical, ecological, and metabolic constraints that are poorly understood, oversimplified, or missing in global biogeochemical methane models. This proposal targets the role of oxygen limitation on methane processes in soil domains, to improve reactive transport models of microbial carbon cycling across terrestrial-aquatic soils and generate data on nutrient cycling activities in Great Lake wetlands. This information could provide new insights into the microbial controllers of Lake Erie eutrophication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Advantages of a variable‐resolution global climate model in reproducing the seasonal evolution of East Asian summer monsoon

Abstract The East Asian summer monsoon (EASM) is unique among monsoon systems that it features meridional evolution of the summer monsoon. In this study, we evaluate the performances of a Variable‐Resolution Community Earth System Model (VR‐CESM) regionally refined over eastern China (14 km) in reproducing the seasonal evolution of EASM precipitation over China. Compared with reference datasets, VR‐CESM shows better performance than the corresponding globally uniform coarse‐resolution model CESM (quasi‐uniform 1°), especially over western China where complex local topography exists. The northward monsoon migration is closely related to low‐level southerly flows and vertical moisture advection, which are more reasonably simulated in VR‐CESM. The four critical timings of the EASM (monsoon onset, withdrawal, peak, and duration) are also better captured in VR‐CESM than in CESM. The corresponding spatial Pearson correlation coefficients of the four critical timings with respect to reference datasets are about 0.1 higher in VR‐CESM than those in CESM. Both models are most accurate in simulating monsoon onset and least accurate at simulating the monsoon peak. The overestimated zonal thermal contrast in CESM is responsible for the earlier monsoon onset and excessive precipitation in September over the Yangtze River valley. Finer resolution in VR‐CESM, especially over the Tibetan Plateau (TP), appears to be a main factor in simulating better zonal thermal contrast and seasonal evolution of the EASM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying Impacts of Renewable Electricity Deployment on Air Quality and Human Health in Southeast Asia Based on AIMS III Scenarios

This study augments the ASEAN Interconnection Masterplan Study III (AIMS III) by quantifying changes to air quality and human health that result from its renewable integration and transmission interconnection scenarios. Performing this analysis requires translation of the changes in projected generation from different power sector fuel sources in the AIMS III scenarios to changes in air pollutant emissions, developing what is known as an emissions inventory for each scenario and year evaluated. An emissions inventory represents who emits air pollutants, from where the pollutants are emitted, when, and how much of which air pollutants are emitted. With the assistance of the ASEAN Centre for Energy and leveraging the best available in-region public data sources, the National Renewable Energy Laboratory (NREL) team compiled a detailed inventory of power plants in the ASEAN region, mapping their PM 2.5 , sulfur oxides (SOx), and nitrogen oxides (NOx) emissions. A new, first-of-its-kind, and user-friendly global air quality model, Global InMAP (Thakrar et al. 2022), is then used to transform the inventory of changes in emissions to changes in concentration of fine particulate matter. Global InMAP then maps the location of human populations in ASEAN countries to calculate humans' exposure to PM 2.5 and estimate excess PM 2.5 -caused mortality attributable to thermal generation sources, as modeled in the AIMS III scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Residential energy demand, emissions, and expenditures at regional and income-decile level for alternative futures

Income and its distribution profile are important determinants of residential energy demand and carry direct implications for human well-being and climate. We explore the sensitivity of residential energy systems to income growth and distribution across shared socioeconomic pathway-representative concentration pathways scenarios using a global, integrated, multisector dynamics model, Global Change Analysis Model, which tracks national/regional household energy services and fuel choice by income decile. Nation/region energy use patterns across deciles tend to converge over time with aggregate income growth, as higher-income consumers approach satiation levels in floorspace and energy services. However, in some regions, existing within-region inequalities in energy consumption persist over time due to slow income growth in lower income groups. Due to continued differences in fuel types, lower income groups will have higher exposure to household air pollution, despite lower contributions to greenhouse gas emissions. We also find that the share of income dedicated to energy is higher for lower deciles, with strong regional differences.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Big Hill Geomechanical Analysis Using M-D Viscoplastic Material Model

The integrity of wellbores at the interbed between the caprock and salt is a serious concern in the Big Hill site. For the remediation and life extension of wellbores, more accurate predictions from the global model are needed. The Big Hill global model is improved using the M-D viscoplastic contact surface model and the mesh containing the interbed layer with contact surfaces between the salt and caprock layers, and fault blocks in overburden and caprock layers. The model calibration has been performed based on the cavern volumetric closures obtained from the Caveman calculations. The results agree well from 1991 to the early 2000s. The difference starts to widen after that, it might be because of frequent fluid movement and raw water injection. Therefore, the predictions from this improved model could be used to examine the structural integrity of caverns in Big Hill salt dome.

42 ENGINEERING↗
DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2021

Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations

We examine the resolution dependence of errors in extreme sub-daily precipitation in available high-resolution climate models. We find that simulated extreme precipitation increases as horizontal resolution increases but that appropriately constructed model skill metrics do not significantly change. We find little evidence that simulated extreme winter or summer storm processes significantly improve with the resolution because the model performance changes identified are consistent with expectations from scale dependence arguments alone. We also discuss the implications of these scale-dependent limitations on the interpretation of simulated extreme precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating fine-resolution, regional outputs of a variable resolution global climate model

Climate models have been used to study water resources and regional hydrologic responses to climate change, but climate model outputs must be downscaled to provide relevant regional data. However, the accuracy of this regional data is limited by uncertainties across and within downscaling methods, uncertainty across global outputs, and discontinuities at downscaled boundaries. A new alternative to traditional downscaling is a variable resolution model that incorporates fine-resolution regions directly into a coarse-resolution, global climate simulation in order to capture contiguous dynamics across resolution boundaries. In this study, we used the Variable-Resolution Community Earth System Model (VR-CESM) to generate one-eighth degree (14 km) fine-resolution outputs for the western U.S. and eastern China from 1970-2006. We focus our evaluation on precipitaiton, temperature, snow pack, solar radiation, and wind. We compare the model outputs with remote-sensing-based precipitation data, and both reanalysis and gridded weather station data for precipitation and temperature. VR-CESM generally has a cold bias in winter and a warm bias in summer in the western U.S., which compensate each other to reduce the annual bias. In eastern China, however, the sign of temperature biases are more consistent throughout the year with cold biases in the higher mountains and warm biases throughout most of the rest of the region. Precipitation biases are dependent upon reference data, and show slight overestimation in high mountain regions in both the U.S. and China with respect to gridded weather station data. Simulated snow cover in the western U.S. is reasonable compared to remote sensing data, but snow cover and snow water equivalent have larger biases when compared to reanalysis data. In eastern China there are widespread snow cover biases compared to remote sensing data. VR-CESM underestimates downward shortwave radiation to a greater degree in summer than in winter, and underestimates surface layer windspeed over mountains to a greater degree than in other areas. Comparison between VR-CESM and a coarser simulation (1-degree Beijing Climate Center model) shows reduced precipitation biases in the mountainous regions with finer resolution, indicating the value of variable-resolution modeling for reigonal studies.

Di Vittorio, Alan↗