Search NASA⌕ Search

SEARCH · Search NASA

Results for “Model Coupling”

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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Assessment of the Pan-Arctic Accelerated Rate of Sea Ice Decline in CMIP6 Historical Simulations

Abstract Arctic sea ice loss in response to a warming climate is assessed in 42 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Sea ice observations show a significant acceleration in the rate of decline commencing near the turn of the twenty-first century. It is our assertion that state-of-the-art climate models should qualitatively reflect this accelerated trend within the limitations of internal variability and observational uncertainty. Our analysis shows that individual CMIP6 simulations of sea ice depict a wide range of model spread on biases and anomaly trends both across models and among their ensemble members. While the CMIP6 multimodel mean captures the observed sea ice area (SIA) decline relatively well, an individual model’s ability to represent the acceleration in sea ice decline remains a challenge. Seventeen (40%) out of 42 CMIP6 models and 37 (13%) out of the total 286 ensemble members reasonably capture the observed trends and acceleration in SIA decline. In addition, a larger ensemble size appears to increase the odds for a model to include at least one ensemble member skillfully representing the accelerated SIA trends. Simulations of sea ice volume (SIV) show much larger spread and uncertainty than SIA; however, due to limited availability of sea ice thickness data, these are not as well constrained by observations. Finally, we find that models with more ocean heat transport simulate larger sea ice declines, which suggests an emergent constraint in CMIP6 ensembles. This relationship points to the need for better understanding and modeling of ice–ocean interactions, especially with respect to frazil ice growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Subpolar North Atlantic Mean State Affects the Response of the Atlantic Meridional Overturning Circulation to the North Atlantic Oscillation in CMIP6 Models

Abstract The Atlantic meridional overturning circulation (AMOC) plays an important role in climate, transporting heat and salt to the subpolar North Atlantic. The AMOC’s variability is sensitive to atmospheric forcing, especially the North Atlantic Oscillation (NAO). Because AMOC observations are short, climate models are a valuable tool to study the AMOC’s variability. Yet, there are known issues with climate models, like uncertainties and systematic biases. To investigate this, preindustrial control experiments from models participating in the phase 6 of Coupled Model Intercomparison Project (CMIP6) are evaluated. There is a large, but correlated, spread in the models’ subpolar gyre mean surface temperature and salinity. By splitting models into groups of either a warm–salty or cold–fresh subpolar gyre, it is shown that warm–salty models have a lower sea ice cover in the Labrador Sea and, hence, enable a larger heat loss during a positive NAO. Stratification in the Labrador Sea is also weaker in warm–salty models, such that the larger NAO-related heat loss can also affect greater depths. As a result, subsurface density anomalies are much stronger in the warm–salty models than in those that tend to be cold and fresh. As these anomalies propagate southward along the western boundary, they establish a zonal density gradient anomaly that promotes a stronger delayed AMOC response to the NAO in the warm–salty models. These findings demonstrate how model mean state errors are linked across variables and affect variability, emphasizing the need for improvement of the subpolar North Atlantic mean states in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Significant and Inevitable End-of-Twenty-First-Century Advances in Surface Runoff Timing in California’s Sierra Nevada

Abstract Using hybrid dynamical–statistical downscaling, 3-km-resolution end-of-twenty-first-century runoff timing changes over California’s Sierra Nevada for all available global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are projected. All four representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change’s Fifth Assessment Report are examined. These multimodel, multiscenario projections allow for quantification of ensemble-mean runoff timing changes and an associated range of possible outcomes due to both intermodel variability and choice of forcing scenario. Under a “business as usual” forcing scenario (RCP8.5), warming leads to a shift toward much earlier snowmelt-driven surface runoff in 2091–2100 compared to 1991–2000, with advances of as much as 80 days projected in the 35-model ensemble mean. For a realistic “mitigation” scenario (RCP4.5), the ensemble-mean change is smaller but still large (up to 30 days). For all plausible forcing scenarios and all GCMs, the simulated changes are statistically significant, so that a detectable change in runoff timing is inevitable. Even for the mitigation scenario, the ensemble-mean change is approximately equivalent to one standard deviation of the natural variability at most elevations. Thus, even when greenhouse gas emissions are curtailed, the runoff change is climatically significant. For the business-as-usual scenario, the ensemble-mean change is approximately two standard deviations of the natural variability at most elevations, portending a truly dramatic change in surface hydrology by the century’s end if greenhouse gas emissions continue unabated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States

This dataset comprises high-resolution climate projections at 1/24 degree grid (~4km) over the conterminous United States (CONUS) based on ten Global Climate Models (GCMs) that are part of the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two artificial intelligence (AI) techniques, primarily based on the computer vision approach called super-resolution. We train two separate networks: super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial network (SRGAN). The networks are trained using Daymet observations, originally available at a 1 km resolution. For training purposes, the Daymet data is interpolated to 1/24 degree (~4km), 0.25 degree and 1 degree, which serve as high, intermediate and low-resolution inputs respectively. For each of the SRCNN and SRGAN network, we use a two-step resolution enhancement, the first step generates 4x refinement from 1 degree to 0.25 degree and the second step generates 6x refinement from 0.25 degree to 1/24 degree (~4km). We downscale daily scale precipitation, maximum temperature and minimum temperature for the six CMIP6 GCMs for 1980 to 2019 in the historical period and 2020 to 2059 in the near-term future under the shared socioeconomic pathway 585 and 245 (SSP585 and SSP245) emission scenarios. We also perform double bias-correction with Daymet observations using a quantile mapping approach, first for GCMs prior to making predictions at 1 degree grid and second after making final predictions at ~4km.

13 HYDRO ENERGY↗
DOE OSTI2023

Future climate change in the Agulhas system and its associated impact on South African rainfall

South African climate variability has been linked to changes in both the Agulhas system and external forcing (i.e. CO 2 and ozone). We analysed future climate change in the Agulhas system volume transport and its associated impacts on South Africa’s precipitation using the Community Climate System Model version 4 as part of the Coupled Model Intercomparison Project, phase 5. Output from one historical and three future greenhouse gas emission scenarios were examined to project various climate storylines. We found that the Agulhas Current volume transport decreases across all three scenarios and that the current displays a strong baroclinic component with an increase in transport at the surface and decrease at intermediate depths. Agulhas leakage was found to increase with historical emissions. Additionally, an east-west dipole pattern for convective precipitation was found over South Africa, with an increase over the eastern region related to an increase in greenhouse gas emissions and a decrease in the western region linked to the location of Hadley cell edge latitude. Moving into the 21st century, future predictions in regional climate variability are shown to be dependent on the intensity of greenhouse gas emissions and are extremely important for South Africa, a region prone to drought and flooding and home to a large population dependent on rain-fed agriculture.Significance:Future climate variability in the Agulhas system and South African region is heavily dependent on changes in external forcing.The Agulhas Current volume transport decreases as the greenhouse gas emissions continue to increase and a strong baroclinic component is found with an increase in transport at the surface and a decrease at the intermediate depths.A strong east-west dipole precipitation pattern is found over South Africa with the increase in the eastern region related to the increase in greenhouse gas emissions and the decrease in the western region related to the location of the Hadley cell edge latitude.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Final Report for UW-Madison Portion of DE-SC0005301, "Collaborative Project: Pacific Decadal Variability and Central Pacific Warming El Niño in a Changing Climate"

This project funded two efforts at understanding the interactions between Central Pacific ENSO events, the mid-latitude atmosphere, and decadal variability in the Pacific. The first was an investigation of conditions that lead to Central Pacific (CP) and East Pacific (EP) ENSO events through the use of linear inverse modeling with defined norms. The second effort was a modeling study that combined output from the National Center for Atmospheric Research (NCAR) Community Atmospheric Model (CAM4) with the Battisti (1988) intermediate coupled model. The intent of the second activity was to investigate the relationship between the atmospheric North Pacific Oscillation (NPO), the Pacific Meridional Mode (PMM), and ENSO. These two activities are described herein.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Interactive Correlation Analysis and Visualization of Climate Data

The relationship between our ability to analyze and extract insights from visualization of climate model output and the capability of the available resources to make those visualizations has reached a crisis point. The large volume of data currently produced by climate models is overwhelming the current, decades-old visualization workflow. The traditional methods for visualizing climate output also have not kept pace with changes in the types of grids used, the number of variables involved, and the number of different simulations performed with a climate model or the feature-richness of high-resolution simulations. This project has developed new and faster methods for visualization in order to get the most knowledge out of the new generation of high-resolution climate models. While traditional climate images will continue to be useful, there is need for new approaches to visualization and analysis of climate data if we are to gain all the insights available in ultra-large data sets produced by high-resolution model output and ensemble integrations of climate models such as those produced for the Coupled Model Intercomparison Project. Towards that end, we have developed new visualization techniques for performing correlation analysis. We have also introduced highly scalable, parallel rendering methods for visualizing large-scale 3D data. This project was done jointly with climate scientists and visualization researchers at Argonne National Laboratory and NCAR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Heat Pump Water Heater Using Wrapped-Tank Microchannel Condenser Coil for Charge Reduction – FY19 1st Quarter (Regular) Milestone Report: Development of Wrapped-Tank Condenser Model (Round Tube and Microchannel), Coupled with Water Tank Model

We recently grew the DOE/HPDM model library to enhance its heat pump water heater (HPWH) design and simulation capability. The new additions include wrapped-tank round tube coil, microchannel coil and a one-dimensional stratified water tank. A wrapped-tank condenser coil has strong coupling with a stratified water tank, which leads HPWH simulation a transient process. The new one-dimensional stratified water tank model is an improvement to the open-source EnergyPlus water tank model, by introducing a calibration factor to account for bulk mixing effect due to water draws and circulations, etc. The wrapped-tank condenser coil model, using a segment-to-segment modeling approach to simulate round tubes or microchannel ports. The heat exchanger models can accept arbitrary refrigerant types, e.g. HFCs, HFOs, natural refrigerants like CO 2 and propane, etc.

42 ENGINEERING↗
DOE OSTI2019

A Themodynamically Consistent Fractional Visco-Elasto-Plastic Model with Memory-Dependent Damage for Anomalous Materials

We develop a thermodynamically consistent, fractional visco-elastoplastic model coupled with damage for anomalous materials. The model utilizes Scott-Blair rheological elements for both visco- elastic/plastic parts. The constitutive equations are obtained through Helmholtz free-energy potentials for Scott-Blair elements, together with a memory-dependent fractional yield function and dissipation inequalities. A memory-dependent Lemaitre-type damage is introduced through fractional damage energy release rates. For time-fractional integration of the resulting nonlinear system of equations, we develop a first-order semi-implicit fractional return-mapping algorithm. We also develop a finite-difference discretization for the fractional damage energy release rate, which results into Hankel-type matrix-vector operations for each time-step, allowing us to reduce the computational complexity from O(N 3 ) to O(N 2 ) through the use of Fast Fourier Transforms. Our numerical results demonstrate that the fractional orders for visco-elasto-plasticity play a crucial role in damage evolution, due to the competition between the anomalous plastic slip and bulk damage energy release rates.

36 MATERIALS SCIENCE↗
DOE OSTI2016

ENSO-Related Precipitation and Its Statistical Relationship with the Walker Circulation Trend in CMIP5 AMIP Models

Observational evidence shows that the Walker circulation (WC) in the tropical Pacific has strengthened in recent decades. In this study, we examine the WC trend for 1979–2005 and its relationship with the precipitation associated with the El Niño Southern Oscillation (ENSO) using the sea surface temperature (SST)-constrained Atmospheric Model Intercomparison Project (AMIP) simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. All of the 29 models show a strengthening of the WC trend in response to an increase in the SST zonal gradient along the equator. Despite the same SST-constrained AMIP simulations, however, a large diversity is found among the CMIP5 climate models in the magnitude of the WC trend. The relationship between the WC trend and precipitation anomalies (PRCPAs) associated with ENSO (ENSO-related PRCPAs) shows that the longitudinal position of the ENSO-related PRCPAs in the western tropical Pacific is closely related to the magnitude of the WC trend. Specifically, it is found that the strengthening of the WC trend is large (small) in the CMIP5 AMIP simulations in which the ENSO-related PRCPAs are located relatively westward (eastward) in the western tropical Pacific. Furthermore, the zonal shift of the ENSO-related precipitation in the western tropical Pacific, which is associated with the climatological mean precipitation in the tropical Pacific, could play an important role in modifying the WC trend in the CMIP5 climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

This paper presents a modeling study conducted on the central Oregon coast for wave resource characterization using the unstructured-grid SWAN model coupled with a nested-grid WWIII model. The flexibility of models of various spatial resolutions and the effects of open- boundary conditions simulated by a nested-grid WWIII model with different physics packages were evaluated. The model results demonstrate the advantage of the unstructured-grid modeling approach for flexible model resolution and good model skills in simulating the six wave resource parameters recommended by the International Electrotechnical Commission in comparison to the observed data in Year 2009 at National Data Buoy Center Buoy 46050. Notably, spectral analysis indicates that the ST4 physics package improves upon the model skill of the ST2 physics package for predicting wave power density for large waves, which is important for wave resource assessment, device load calculation, and risk management. In addition, bivariate distributions show the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than that with the ST2 physics package. This study demonstrated that the unstructured-grid wave modeling approach, driven by the nested-grid regional WWIII outputs with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (10^2 km).

Wu, Wei-Cheng↗
DOE OSTI2018

Investigating the impacts of Saharan dust on tropical deep convection using spectral bin microphysics

Abstract. To better understand the impacts of dust aerosols on deep convective cloud (DCC) systems reported by previous observational studies, a case study in the tropical eastern Atlantic was investigated using the Weather Research and Forecasting (WRF) model coupled with a spectral bin microphysics (SBM) model. A detailed set of ice nucleation parameterizations linking ice formation with aerosol particles has been implemented in the SBM. Increasing ice nuclei (IN) concentration in the dust cases results in the formation of more numerous small ice particles in the heterogeneous nucleation regime (between −5 and −38 °C) compared to the background ( Clean ) case. Convective updrafts are invigorated by increased latent heat release due to depositional growth and riming of these more numerous particles, which results in increased overshooting and higher convective core top heights. Competition between the more numerous particles for available water vapor during diffusional growth and available smaller crystals and/or drops during collection reduces particle growth rates and shifts precipitation formation to higher altitudes in the heterogeneous nucleation regime. A greater number of large snow particles form in the dust cases, which are transported from the core into the stratiform regime and sediment out quickly. Together with reduced homogeneous ice formation, the stratiform and/or anvil cloud occurrence shifts frequency to warmer temperatures and reduces anvil cloud extents. Total surface precipitation accumulation is reduced proportionally as IN concentration is increased; though the stratiform precipitation accumulation is increased due to greater snow formation and growth, it does not counteract the reduced convective accumulation due to less efficient graupel formation. Radar reflectivity values are increased in the dust cases at temperatures below 0 °C in both the convective and stratiform regimes due to more large snow particles, and reduced in the convective core near the surface due to melt of small ice or graupel particles, consistent with case study observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reaching 1.5 and 2.0 °C global surface temperature targets using stratospheric aerosol geoengineering

A new set of stratospheric aerosol geoengineering (SAG) model experiments has been performed with Community Earth System Model version 2 (CESM2) with the Whole Atmosphere Community Climate Model (WACCM6) that are based on the Coupled Model Intercomparison Project phase 6 (CMIP6) overshoot scenario (SSP5-34-OS) as a baseline scenario to limit global warming to 1.5 or 2.0 °C above 1850–1900 conditions. The overshoot scenario allows us to applying a peak-shaving scenario that reduces the needed duration and amount of SAG application compared to a high forcing scenario. In addition, a feedback algorithm identifies the needed amount of sulfur dioxide injections in the stratosphere at four pre-defined latitudes, 30 °N, 15 °N, 15 °S, and 30 °S, to reach three surface temperature targets: global mean temperature, and interhemispheric and pole-to-Equator temperature gradients. These targets further help to reduce side effects, including overcooling in the tropics, warming of high latitudes, and large shifts in precipitation patterns. These experiments are therefore relevant for investigating the impacts on society and ecosystems. Comparisons to SAG simulations based on a high emission pathway baseline scenario (SSP5-85) are also performed to investigate the dependency of impacts using different injection amounts to offset surface warming by SAG. We find that changes from present-day conditions around 2020 in some variables depend strongly on the defined temperature target (1.5 °C vs. 2.0 °C). These include surface air temperature and related impacts, the Atlantic Meridional Overturning Circulation, which impacts ocean net primary productivity, and changes in ice sheet surface mass balance, which impacts sea level rise. Others, including global precipitation changes and the recovery of the Antarctic ozone hole, depend strongly on the amount of SAG application. Furthermore, land net primary productivity as well as ocean acidification depend mostly on the global atmospheric CO 2 concentration and therefore the baseline scenario. Multi-model comparisons of experiments that include strong mitigation and carbon dioxide removal with some SAG application are proposed to assess the robustness of impacts on societies and ecosystems.

42 ENGINEERING↗
NASA NTRS1989

Simulated appearance of an evolving supercell hail storm at SSM/I microwave frequencies

The contribution of distinct classes of hydrometeors generated from a cloud model to top-of-atmosphere microwave brightness temperatures is studied at the four separate frequencies of the Special Sensor Microwave Imager (SSM/I). The relative radiative role of individual modes of hydrometeors during the evolution of a severe thunderstorm is examined using a microwave radiative transfer model coupled with a three-dimensional cloud model capable of simulating the essential dynamical and microphysical features of an intense hail store. Consideration is given to plans to use the simulation results with actual multichannel measurements from SSM/I to provide a framework for multispectral precipitation retrieval algorithms.

Smith, Eric A.↗
NASA NTRS1993

Mutual Inductance Problem for a System Consisting of a Current Sheet and a Thin Metal Plate

Rapid inspection of aircraft structures for flaws is of vital importance to the commercial and defense aircraft industry. In particular, inspecting thin aluminum structures for flaws is the focus of a large scale R&D effort in the nondestructive evaluation (NDE) community. Traditional eddy current methods used today are effective, but require long inspection times. New electromagnetic techniques which monitor the normal component of the magnetic field above a sample due to a sheet of current as the excitation, seem to be promising. This paper is an attempt to understand and analyze the magnetic field distribution due to a current sheet above an aluminum test sample. A simple theoretical model, coupled with a two dimensional finite element model (FEM) and experimental data will be presented in the next few sections. A current sheet above a conducting sample generates eddy currents in the material, while a sensor above the current sheet or in between the two plates monitors the normal component of the magnetic field. A rivet or a surface flaw near a rivet in an aircraft aluminum skin will disturb the magnetic field, which is imaged by the sensor. Initial results showed a strong dependence of the flaw induced normal magnetic field strength on the thickness and conductivity of the current-sheet that could not be accounted for by skin depth attenuation alone. It was believed that the eddy current imaging method explained the dependence of the thickness and conductivity of the flaw induced normal magnetic field. Further investigation, suggested the complexity associated with the mutual inductance of the system needed to be studied. The next section gives an analytical model to better understand the phenomenon.

Fulton, J. P.↗