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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Toward more realistic projections of soil carbon dynamics by Earth system models

Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool-and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. Furthermore, we recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Subtropical Eastern North Pacific SST bias in earth system models

Karthik Balaguru (PNNL), Luke Van Roekel (LLNL), L. Ruby Leung (PNNL) and Milena Veneziani (LLNL) Abstract: This study systematically evaluates the warm SST bias in the Subtropical Eastern North Pacific (SENP), a problem plaguing most CMIP6 models, using the Energy Exascale Earth System Model version 1 (E3SM). Compared to the model at its standard resolution (1o atmosphere, 30-60 km ocean), the SST bias improves in several regions at high resolution (0.25o atmosphere, 18-6 km ocean) except in subtropical eastern boundary regions, such as the SENP. A mixed layer heat budget analysis reveals that errors in the surface radiative fluxes and horizontal heat advection are primarily responsible for the bias. Analysis of HighResMIP models indicates that the lack of improvement in the SENP SST bias with resolution is not native to E3SM alone. A larger CMIP6 inter-model correlation of SST bias with biases in surface winds than radiation highlights the important role of large-scale circulation biases in the SENP SST bias in coupled models.

Balaguru, Karthik↗
DOE OSTI2017

A New Discrete Element Sea-Ice Model for Earth System Modeling

Sea ice forms a frozen crust of sea water oating in high-latitude oceans. It is a critical component of the Earth system because its formation helps to drive the global thermohaline circulation, and its seasonal waxing and waning in the high north and Southern Ocean signi cantly affects planetary albedo. Usually 4{6% of Earth's marine surface is covered by sea ice at any one time, which limits the exchange of heat, momentum, and mass between the atmosphere and ocean in the polar realms. Snow accumulates on sea ice and inhibits its vertical growth, increases its albedo, and contributes to pooled water in melt ponds that darken the Arctic ice surface in the spring. Ice extent and volume are subject to strong seasonal, inter-annual and hemispheric variations, and climatic trends, which Earth System Models (ESMs) are challenged to simulate accurately (Stroeve et al., 2012; Stocker et al., 2013). This is because there are strong coupled feedbacks across the atmosphere-ice-ocean boundary layers, including the ice-albedo feedback, whereby a reduced ice cover leads to increased upper ocean heating, further enhancing sea-ice melt and reducing incident solar radiation re ected back into the atmosphere (Perovich et al., 2008). A reduction in perennial Arctic sea-ice during the satellite era has been implicated in mid-latitude weather changes, including over North America (Overland et al., 2015). Meanwhile, most ESMs have been unable to simulate observed inter-annual variability and trends in Antarctic sea-ice extent during the same period (Gagne et al., 2014).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Introducing Multisensor Satellite Radiance-Based Evaluation for Regional Earth System Modeling

Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

Planetary Boundary Layer↗
DOE OSTI2019

Fldgen v1.0: an emulator with internal variability and space–time correlation for Earth system models

Earth system models (ESMs) are the gold standard for producing future projections of climate change, but running them is difficult and costly, and thus researchers are generally limited to a small selection of scenarios. This paper presents a technique for detailed emulation of the Earth system model (ESM) temperature output, based on the construction of a deterministic model for the mean response to global temperature. The residuals between the mean response and the ESM output temperature fields are used to construct variability fields that are added to the mean response to produce the final product. The method produces grid-level output with spatially and temporally coherent variability. Output fields include random components, so the system may be run as many times as necessary to produce large ensembles of fields for applications that require them. We introduce the method, show example outputs, and present statistical verification that it reproduces the ESM properties it is intended to capture. This method, available as an open-source R package, should be useful in the study of climate variability and its contribution to uncertainties in the interactions between human and Earth systems.

58 GEOSCIENCES↗
DOE OSTI2024

Modeling hydrogen markets: Energy system model development status and decarbonization scenario results

Hydrogen can be used as an energy carrier and chemical feedstock to reduce greenhouse gas emissions, especially in difficult-to-decarbonize markets such as medium- and heavy-duty vehicles, aviation and maritime, iron and steel, and the production of fuels and chemicals. Significant literature has been accumulated on engineering-based assessments of various hydrogen technologies, and real-world projects are validating technology performance at larger scales and for low-carbon supply chains. While energy system models continue to be updated to track this progress, many are currently limited in their representation of hydrogen, and as a group they tend to generate highly variable results under decarbonization constraints. Here, the present work provides insights into the development status and decarbonization scenario results of 15 energy system models participating in study 37 of the Stanford Energy Modeling Forum (EMF37), focusing on the U.S. energy system. The models and scenario results vary widely in multiple respects: hydrogen technology representation, scope and type of hydrogen end-use markets, relative optimism of hydrogen technology input assumptions, and market uptake results reported for 2050 under various decarbonization assumptions. Most models report hydrogen market uptake increasing with decarbonization constraints, though some models report high carbon prices being required to achieve these increases and some find hydrogen does not compete well when assuming optimistic assumptions for all advanced decarbonization technologies. Across various scenarios, hydrogen market success tends to have an inverse relationship to success with direct air capture (DAC) and carbon capture and storage (CCS) technologies. While most model-scenario combinations predict modest hydrogen uptake by 2050 – <10 million metric tons (MMT) – aggregating the top 10 % of market uptake results across sectors suggests an upper range demand potential of 42–223 MMT. The high degree of variability across both modeling methods and market uptake results suggests that increased harmonization of both input assumptions and subsector competition scope would lead to more consistent results across energy system models. The wide variability in results indicates strongly divergent conclusions on the role of hydrogen in a decarbonized energy future.

08 HYDROGEN↗
DOE OSTI2020

Initial Land Use/Cover Distribution Substantially Affects Global Carbon and Local Temperature Projections in the Integrated Earth System Model

Initial land cover distribution varies among Earth system models, an uncertainty in initial conditions that can substantially affect carbon and climate projections. We use the integrated Earth System Model to show that a 3.9 M km2 difference in 2005 global forest area (9–14% of total forest area) generates uncertainties in initial atmospheric CO2 concentration, terrestrial carbon, and local temperature that propagate through a future simulation following the Representative Concentration Pathway 4.5. By 2095, the initial 6 ppmv uncertainty range increases to 9 ppmv and the initial 26 PgC uncertainty range in terrestrial carbon increases to 33 PgC. The initial uncertainty range in annual average local temperature of -0.74 to 0.96 °C persists throughout the future simulation, with a seasonal maximum in Dec-Jan-Feb. These results highlight the importance of accurately characterizing historical land use and land cover to reduce overall initial condition uncertainty.

Di Vittorio, Alan↗
DOE OSTI2017

PhotoSketch: a photocentric urban 3D modeling system

Online mapping services from Google, Apple, and Microsoft are exceedingly popular applications for exploring 3D urban cities. Their explosive growth provides impetus for photorealistic 3D modeling of urban scenes. Although classical algorithms such as multiview stereo and laser range scanners are traditional sources for detailed 3D models of existing structures, they generate heavyweight models that are not appropriate for the streaming data that these navigation applications leverage. Instead, lightweight models as produced by interactive image-based tools are better suited for this domain. The contribution of this work is that it merges the benefits of multiview geometry, an intuitive sketching interface, and dynamic texture mapping to produce lightweight photorealistic 3D models of buildings. We present experimental results from urban scenes using our PhotoSketch system.

3D photography↗
DOE OSTI2017

Conversion and Validation of Distribution System Model from a QSTS-Based Tool to a Real-Time Dynamic Phasor Simulator

A software process is developed to convert distribution network models from a quasi-static time-series tool (OpenDSS) to a real-time dynamic phasor simulator (ePHASORSIM). The description of this process in this paper would be helpful for researchers who intend to perform similar conversions. The converter could be utilized directly by users of real-time simulators who intend to perform software-in-the-loop or hardware-in-the-loop tests on large distribution test feeders for a range of use cases, including testing functions of advanced distribution management systems against a simulated distribution system. In the future, the developers intend to release the conversion tool as open source to enable use by others.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

One of These Things IS Like the Other: Pursuing a New Taxonomy of Industry for Improved Energy System Modeling

Industrial processes drive the exchange of materials, energy, and currency throughout the economy. These processes are powered by electricity and direct combustion, with variation in their operation even within the same industry. This heterogeneity makes it difficult for large models, including the National Energy Modeling System (US), to project their energy use while remaining tractable. Decarbonization and ensuing changes to the energy system require changes to industrial processes while offering opportunities for process innovation, but the extent and nature of changes are difficult to model with current classification schemes and corresponding data. The North American Industrial Classification (NAICS) is an economic taxonomy of industries, but its categories are less meaningful from an energy and material flow perspective. For example, a facility that makes steel from iron ore in a blast furnace/basic oxygen furnace is categorized under the same NAICS code as a facility that makes steel from scrap in an electric arc furnace despite the scale, use of recycled scrap versus iron ore, and energy use differences in the two facility types. Exploratory analysis is performed on a large dataset used for plant-level energy assessment in order to detect clusters that can aid in better modeling of industry for energy analysis in an evolving system with breakthrough technologies.

28 EE - Advanced Manufacturing Office (EE-5A)↗
NASA NTRSDate not supplied

Tracking 2020 Decreases in Carbon Dioxide Due to the COVID19 Pandemic in NASA’s GEOS Modeling system: Implications for Space-Based Carbon Monitoring

The COVID19 pandemic led to abrupt, worldwide changes in human activity and related emissions of air pollutants and greenhouse gases that are unprecedented in modern times. NASA satellites have demonstrated their ability to observe some of these impacts, particularly in relation to short-lived gases like nitrogen dioxide (NO2), which is emitted primarily from transportation. Bottom-up analyses of carbon dioxide (CO2) emissions suggest that the growth of atmospheric CO2 has also slowed, but differences are much more subtle than for NO2 because of the lifetime of atmospheric CO2 and sectoral differences in emission reductions. Estimates of CO2 from NASA’s Orbiting Carbon Observatory 2 (OCO-2) provide a unique view of COVID19 impacts, but observed changes in the column-average CO2 can be difficult to interpret because of gaps in spatial coverage. Assimilating these data into the Goddard Earth Observing System (GEOS), an integrated Earth system model with an advanced Constituent Data Assimilation System (CoDAS), helped to reveal changes in CO2 that are consistent with separate, bottom-up analyses of emissions reductions. Both indicate that from February-April of 2020, the growth in CO2 over Europe, North America, and Asia was roughly 0.3 ppm less than during the previous four years. Anomalies derived from gap-filled GEOS OCO-2 CoDAS products contribute to a joint effort by the world’s space agencies to track COVID19 impacts on the Earth. However, attribution of these changes is complicated by interannual variability in atmospheric circulation and the influence of climate on ocean and land carbon sinks. We discuss these results from the perspective of space-based carbon monitoring, which has received considerable support over the past decade from NASA. Our results demonstrate the accomplishments of current sensors and data assimilation systems, but also highlight challenges in providing high quality, low latency information to the public. In particular, understanding and attributing CO2 changes during 2020 requires year-specific information about land and ocean fluxes, which is often delayed for months or even years. We discuss current limitations and potential solutions to address these lags, which would support more reliable and timely space-based carbon monitoring.

COVID-19↗
NASA NTRS2022

NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0: Stratospheric Composition

The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-29CF) provides recent estimates and five-day forecasts of atmospheric composition to the public in near-real time. To do this, the GEOS Earth system model is coupled with the GEOS-Chem tropospheric-stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS-CF system is described, including updates made to the GEOS-Chem UCX mechanism within GEOS-CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar and satellite observations for stratospheric composition, including measurements of ozone (O3) and important nitrogen and chlorine species related to stratospheric O3recovery. The GEOS-CF nudges the stratospheric O3towards the GEOS Forward Processing (GEOS FP) assimilated O3product; as a result the stratospheric O3in the GEOS-CF historical estimate agrees well with observations. During abnormal dynamical and chemical environments such as the 2020 polar vortexes, the GEOS-CF O3forecasts are more realistic than GEOS FP O3forecasts because of the inclusion of the complex GEOS-Chem UCX stratospheric chemistry. Overall, the spatial patterns of the GEOS-CF simulated concentrations of stratospheric composition agree well with satellite observations. However, there are notable biases – such as low NOx and HNO3 in the polar regions and generally low HCl throughout the stratosphere – and future improvements to the chemistry mechanism and emissions are discussed. GEOS-CF is a new tool for the research community and instrument teams observing trace gases in the stratosphere and troposphere, providing near-real-time three-dimensional gridded information on atmospheric composition.

GEOS-CF↗
NASA NTRS2003

The Earth System Model

The Earth System Model is the natural evolution of current climate models and will be the ultimate embodiment of our geophysical understanding of the planet. These models are constructed from components - atmosphere, ocean, ice, land, chemistry, solid earth, etc. models and merged together through a coupling program which is responsible for the exchange of data from the components. Climate models and future earth system models will have standardized modules, and these standards are now being developed by the ESMF project funded by NASA. The Earth System Model will have a variety of uses beyond climate prediction. The model can be used to build climate data records making it the core of an assimilation system, and it can be used in OSSE experiments to evaluate. The computing and storage requirements for the ESM appear to be daunting. However, the Japanese ES theoretical computing capability is already within 20% of the minimum requirements needed for some 2010 climate model applications. Thus it seems very possible that a focused effort to build an Earth System Model will achieve succcss.

Schoeberl, Mark↗
NASA NTRS2002

Software Engineering Support of the Third Round of Scientific Grand Challenge Investigations: Earth System Modeling Software Framework Survey

One of the most significant challenges in large-scale climate modeling, as well as in high-performance computing in other scientific fields, is that of effectively integrating many software models from multiple contributors. A software framework facilitates the integration task, both in the development and runtime stages of the simulation. Effective software frameworks reduce the programming burden for the investigators, freeing them to focus more on the science and less on the parallel communication implementation. while maintaining high performance across numerous supercomputer and workstation architectures. This document surveys numerous software frameworks for potential use in Earth science modeling. Several frameworks are evaluated in depth, including Parallel Object-Oriented Methods and Applications (POOMA), Cactus (from (he relativistic physics community), Overture, Goddard Earth Modeling System (GEMS), the National Center for Atmospheric Research Flux Coupler, and UCLA/UCB Distributed Data Broker (DDB). Frameworks evaluated in less detail include ROOT, Parallel Application Workspace (PAWS), and Advanced Large-Scale Integrated Computational Environment (ALICE). A host of other frameworks and related tools are referenced in this context. The frameworks are evaluated individually and also compared with each other.

Talbot, Bryan↗
DOE OSTI2020

Integrated Transmission-and-Distribution System Modeling of Power Systems: State-of-the-Art and Future Research Directions

Integrated transmission-and-distribution (T&D) modeling is a new and developing method for simulating power systems. Interest in integrated T&D modeling is driven by the changes taking place in power systems worldwide that are resulting in more decentralized power systems with increasingly high levels of distributed energy resources. Additionally, the increasing role of the hitherto passive energy consumer in the management and operation of power systems requires more capable and detailed integrated T&D modeling to understand the interactions between T&D systems. Although integrated T&D modeling has not yet found widespread commercial application, its potential for changing the decades-old power system modeling approaches has led to several research efforts in the last few years that tried to (i) develop algorithms and software for steady-state and dynamic modeling of power systems and (ii) demonstrate the advantages of this modeling approach compared with traditional, separated T&D system modeling. In this paper, we provide a review of integrated T&D modeling research efforts and the methods employed for steady-state and dynamic modeling of power systems. We also discuss our current research in integrated T&D modeling and the potential directions for future research. This paper should be useful for power systems researchers and industry members because it will provide them with a critical summary of current research efforts and the potential topics where research efforts are needed to further advance and demonstrate the utility of integrated T&D modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗