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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simulating Observations of Southern Ocean Clouds and Implications for Climate

Southern Ocean (S. Ocean) clouds are important for climate prediction. Yet previous global climate models failed to accurately represent cloud phase distributions in this observation-sparse region. In this study, data from the Southern Ocean Clouds, Radiation, Aerosol, Transport Experimental Study (SOCRATES) experiment is compared to constrained simulations from a global climate model (the Community Atmosphere Model, CAM). Nudged versions of CAM are found to reproduce many of the features of detailed in situ observations, such as cloud location, cloud phase, and boundary layer structure. The simulation in CAM6 has improved its representation of S. Ocean clouds with adjustments to the ice nucleation and cloud microphysics schemes that permit more supercooled liquid. Comparisons between modeled and observed hydrometeor size distributions suggest that the modeled hydrometeor size distributions represent the dual peaked shape and form of observed distributions, which is remarkable given the scale difference between model and observations. Comparison to satellite observations of cloud physics is difficult due to model assumptions that do not match retrieval assumptions. Some biases in the model's representation of S. Ocean clouds and aerosols remain, but the detailed cloud physical parameterization provides a basis for process level improvement and direct comparisons to observations. This is crucial because cloud feedbacks and climate sensitivity are sensitive to the representation of S. Ocean clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating integration and performance of containerized climate applications on a Hewlett Packard Enterprise Cray system

Containers have taken over large swaths of cloud computing as the most convenient way of packaging and deploying applications. The features that containers offer for packaging and deploying applications translate to high performance computing (HPC) as well. At The National Oceanic and Atmospheric Administration, containers provide an easy way to build and distribute complex HPC applications, allowing faster collaboration, portability, and experiment computer environment reproducibility amongst the scientific community. The challenge arises when applications rely on message passing interface (MPI). This necessitates investigation into how to properly run these applications with their own unique requirements and produce performance on par with native runs. We investigate the MPI performance for benchmarks and containerized climate models for various containers covering selection of compiler and MPI library combinations from the Cray provided programming environments on the Cray XC supercomputer GAEA. Performance from the benchmarks and the climate models shows that for the most part containerized applications perform on par with the natively built applications when the system optimized Cray MPICH libraries are bound into the container, and the hybrid model containers have poor performance in comparison. We also describe several challenges and our solutions in running these containers, particularly challenges with heterogeneous jobs for the containerized model runs.

Abraham, Subil↗
NASA NTRS1977

Mt. Agung eruption as a confirmation of the effect of atmospheric radiative perturbations on climate

The effects of the 1963 Mt. Agung (Bali) volcanic eruption are studied with respect to the response of the climate system to global radiative perturbations. Volcanic aerosols, spread globally by stratospheric winds, cause a perturbation characterized by a reasonably well known forcing function. Since the climatic response to a large eruption should be pronounced, these phenomena might provide a means to test the validity of climate models. The Mt. Agung eruption is described, and the atmospheric thermal response is calculated. The magnitude, sign, and time delay of temperature changes computed with a simple one-dimensional climate model for both the stratosphere and troposphere are in agreement with the observed temperature changes.

Hansen, J. E.↗
NASA NTRS2017

Extratropical Weather Systems on Mars: Radiatively-Active Water Ice Effects

Extratropical, large-scale weather disturbances, namely transient, synoptic-period,baroclinic barotropic eddies - or - low- (high-) pressure cyclones (anticyclones), are components fundamental to global circulation patterns for rapidly rotating, differentially heated, shallow atmospheres such as Earth and Mars. Such "wave-like" disturbances that arise via (geophysical) fluid shear instability develop, mature and decay, and travel west-to-east in the middle and high latitudes within terrestrial-like planetary atmospheres. These disturbances serve as critical agents in the transport of heat and momentum between low and high latitudes of the planet. Moreover, they transport trace species within the atmosphere (e.g., water vapor/ice, other aerosols (dust), chemical species, etc). Between early autumn through early spring, middle and high latitudes on Mars exhibit strong equator-to-pole mean temperature contrasts (i.e., "baroclinicity"). Data collected during the Viking era and observations from both the Mars Global Surveyor (MGS) and Mars Reconnaissance Orbiter (MRO) indicate that such strong baroclinicity supports vigorous, large-scale eastward traveling weather systems [Banfield et al., 2004; Barnes et al., 1993]. A good example of traveling weather systems, frontal wave activity and sequestered dust activity from MGS/MOC image analyses is provided in Figure 1 (cf. Wang et al. [2005]). Utilizing an upgraded and evolving version of the NASA Ames Research Center (ARC) Mars global climate model, investigated here are key dynamical and physical aspects of simulated northern hemisphere (NH) large-scale extratropica lweather systems,with and without radiatively-active water ice clouds. Mars Climate Model:

cyclones↗
NASA NTRS2022

NASA Global Daily Downscale Projections, CMIP6

We describe the latest version of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The archive contains downscaled historical and future projections for 1950–2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 1/4-degree horizontal resolution. Currently, eight variables from five CMIP6 experiments (historical, SSP126, SSP245, SSP370, and SSP585) are provided as procurable from thirty-five global climate models.

Climate↗
DOE OSTI2023

Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Array↗
NASA NTRS1988

Modelling the slow climatic attractor

Attempts to model the evolution of the slow-response parts of the climatic system such as the global ice mass are reviewed, and the fundamental laws governing the complete complex multidomain slow system, called the slow climatic attractor, are analyzed. The uses of paleoclimatic records are discussed, and a low-order model for the late Quaternary variations that constitutes a credible global representation of the slow climatic attractor is presented. It is shown that although according to the model the climatic system is centered on an unstable equilibrium, the points of the limit cycle attractor are quite stable and robust. It is also noted that one major deficiency of the model is its inability to portray the geographic spatial variability of climatic mean states. However, at least with regard to atmospheric CO2, estimates of the geographic fields of all significant atmospheric and surface boundary layer climate variables are deducible.

Saltzman, Barry↗
DOE OSTI2024

Timescale Dependence of the Precipitation Response to CO 2 ‐Induced Warming in Millennial‐Length Climate Simulations

Abstract Previous work has shown that estimates of climate sensitivity vary over time in response to abrupt CO 2 forcing in climate model simulations. The energy fluxes that drive warming in response to increasing CO 2 also influence precipitation, which prompts the question: Does the precipitation response therefore also vary over time? We investigate by examining the response of precipitation to warming forced by greenhouse gases—the hydrological sensitivity—in a set of millennial‐length climate simulations with multiple climate models, Long Run Model Intercomparison Project (LongRunMIP). We compare hydrological sensitivity calculated from three different timescales of the simulations: years 1–20, 21–150, and 151–1000. We show that the hydrological sensitivity lacks a consistent dependence on timescale, in contrast to climate sensitivity. Decomposition of the surface energy budget reveals that the relative muting of the multi‐model mean hydrological sensitivity is driven by surface downwelling shortwave flux.

Him (Kinen) Kao, Wing↗
NASA NTRS2019

Stellar Activity Effects on Moist Habitable Terrestrial Atmospheres Around M Dwarfs

Transit spectroscopy of terrestrial planets around nearby M dwarfs will be a primary goal of space missions in coming decades. Three-dimensional climate modeling has shown that slow-synchronous rotating terrestrial planets may develop thick clouds at the substellar point, increasing the albedo. For M dwarfs with T(eff)>3000 K, such planets at the inner habitable zone (IHZ) have been shown to retain moist greenhouse conditions, with enhanced stratospheric water vapor ( fH2O>10(exp −3)) and low Earth-like surface temperatures. However, M dwarfs also possess strong UV activity, which may effectively photolyze stratospheric H2O. Prior modeling efforts have not included the impact of high stellar UV activity on the H2O. Here, we employ a 1D photochemical model with varied stellar UV, to assess whether H2O destruction driven by high stellar UV would affect its detectability in transmission spectroscopy. Temperature and water vapor profiles are taken from published 3D climate model simulations for an IHZ Earth-sized planet around a 3300 K M dwarf with an N2–H2O atmosphere; they serve as self-consistent input profiles for the 1D model. We explore additional chemical complexity within the 1D model by introducing other species into the atmosphere. We find that as long as the atmosphere is well-mixed up to 1 mbar, UV activity appears to not impact detectability of H2O in the transmission spectrum. The strongest H2O features occur in the James Webb Space Telescope MIRI instrument wavelength range and are comparable to the estimated systematic noise floor of ∼50 ppm.

Badhan, Mahmuda Afrin↗
DOE OSTI2025

Cloud radiative effects significantly increase wintertime atmospheric blocking in the Euro-Atlantic sector

Reliable simulation, prediction, and complete theoretical understanding of atmospheric blocking remain challenging despite its significant socio-economic impacts. Generations of climate models have notoriously underestimated blocking frequency, particularly over the Euro-Atlantic sector. Identifying factors controlling blocking frequency and dynamics is therefore essential for improving its simulation. Here, using a cloud-locking experiment, we show that cloud radiative effects (CREs) significantly increase the frequency of Euro-Atlantic blocking. CREs enhance upstream diabatic source of wave activity, both directly through longwave heating and indirectly through their feedback on latent heating, with the latter playing the dominant role. The resulting increase in the upstream diabatic source feeds into local wave activity downstream and promotes blocking formation. Qualitatively similar results are shown by multi-model experiments with radiatively inactive clouds to longwave radiation, albeit with a larger impact from mean-state changes. The results underscore the necessity of accurately representing cloud-radiation interactions in weather and climate models for improved prediction of blocking events.

Atmospheric dynamics↗
NASA NTRS1982

The influence of ground moisture conditions in North America on summer climate as modeled in the GISS GCM

A new general circulation model, developed to run on a coarse grid (8 x 10 deg resolution) at the Goddard institute for Space Studies is employed to investigate the potential use of ground moisture anomalies for seasonal climate prediction. For three different summertime simulations, the ground moisture on 1 June over the United States is reduced to 1/4 of its value in the control run. The results show that the subsequent surface air temperature is significantly higher throughout most of the summer, while the precipitation decreases, especially in June and July. Knowledge of late spring ground moisture anomalies should thus be an aid in predicting summertime climate.

Rind, D.↗
NASA NTRS2019

Stellar Activity Effects on Moist Habitable Terrestrial Atmospheres Around M Dwarfs

Transit spectroscopy of terrestrial planets around nearby M dwarfs is a primary goal of space missions in coming decades. 3-D climate modeling has shown that slow-synchronous rotating terrestrial planets may develop thick clouds at the substellar point, increasing the albedo. For M dwarfs with T(eff) > 3000 K, such planets at the inner habitable zone (IHZ) have been shown to retain moist greenhouse conditions, with enhanced stratospheric water vapor (fH2O > 10(exp -3)) and low Earth-like surface temperatures. However, M dwarfs also possess strong UV activity, which may effectively photolyze stratospheric H2O. Prior modeling efforts have not included the impact of high stellar UV activity on the H2O. Here, we employ a 1-D photochemical model with varied stellar UV, to assess whether H2O destruction driven by high stellar UV would affect its detectability in transmission spectroscopy. Temperature and water vapor profiles are taken from published 3-D climate model simulations for an IHZ Earth-sized planet around a 3300 K M dwarf with an N2-H2O atmosphere; they serve as self-consistent input profiles for the 1-D model. We explore additional chemical complexity within the 1-D model by introducing other species into the atmosphere. We find that as long as the atmosphere is well-mixed up to 1 mbar, UV activity appears to not impact detectability of H2O in the transmission spectrum. The strongest H2O features occur in the JWST MIRI instrument wavelength range and are comparable to the estimated systematic noise floor of ~50 ppm.

Mahmuda Afrin Badhan↗
NASA NTRS2015

Role of Longwave Cloud-Radiation Feedback in the Simulation of the Madden-Julian Oscillation

The role of the cloud-radiation interaction in the simulation of the Madden-Julian oscillation (MJO) is investigated. A special focus is on the enhancement of column-integrated diabatic heating due to the greenhouse effects of clouds and moisture in the region of anomalous convection. The degree of this enhancement, the greenhouse enhancement factor (GEF), is measured at different precipitation anomaly regimes as the negative ratio of anomalous outgoing longwave radiation to anomalous precipitation. Observations show that the GEF varies significantly with precipitation anomaly and with the MJO cycle. The greenhouse enhancement is greater in weak precipitation anomaly regimes and its effectiveness decreases monotonically with increasing precipitation anomaly. The GEF also amplifies locally when convection is strengthened in association with the MJO, especially in the weak precipitation anomaly regime (less than 5 mm day(exp -1)). A robust statistical relationship is found among CMIP5 climate model simulations between the GEF and the MJO simulation fidelity. Models that simulate a stronger MJO also simulate a greater GEF, especially in the weak precipitation anomaly regime (less than 5 mm day(exp -1)). Models with a greater GEF in the strong precipitation anomaly regime (greater than 30 mm day(-1)) represent a slightly slower MJO propagation speed. Many models that lack the MJO underestimate the GEF in general and in particular in the weak precipitation anomaly regime. The results herein highlight that the cloud-radiation interaction is a crucial process for climate models to correctly represent the MJO.

Clouds↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
NASA NTRS1999

Physically-Derived Dynamical Cores in Atmospheric General Circulation Models

The algorithm chosen to represent the advection in atmospheric models is often used as the primary attribute to classify the model. Meteorological models are generally classified as spectral or grid point, with the term grid point implying discretization using finite differences. These traditional approaches have a number of shortcomings that render them non-physical. That is, they provide approximate solutions to the conservation equations that do not obey the fundamental laws of physics. The most commonly discussed shortcomings are overshoots and undershoots which manifest themselves most overtly in the constituent continuity equation. For this reason many climate models have special algorithms to model water vapor advection. This talk focuses on the development of an atmospheric general circulation model which uses a consistent physically-based advection algorithm in all aspects of the model formulation. The shallow-water model is generalized to three dimensions and combined with the physics parameterizations of NCAR's Community Climate Model. The scientific motivation for the development is to increase the integrity of the underlying fluid dynamics so that the physics terms can be more effectively isolated, examined, and improved. The expected benefits of the new model are discussed and results from the initial integrations will be presented.

Rood, Richard B.↗
DOE OSTI2021

Impacts of secondary ice production on Arctic mixed-phase clouds based on ARM observations and CAM6 single-column model simulations

For decades, measured ice crystal number concentrations have been found to be orders of magnitude higher than measured ice-nucleating particle number concentrations in moderately cold clouds. This observed discrepancy reveals the existence of secondary ice production (SIP) in addition to the primary ice nucleation. However, the importance of SIP relative to primary ice nucleation remains highly unclear. Furthermore, most weather and climate models do not represent SIP processes well, leading to large biases in simulated cloud properties. This study demonstrates a first attempt to represent different SIP mechanisms (frozen raindrop shattering, ice–ice collisional breakup, and rime splintering) in a global climate model (GCM). The model is run in the single column mode to facilitate comparisons with the Department of Energy (DOE)'s Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We show the important role of SIP in four types of clouds during M-PACE (i.e., multilayer, single-layer stratus, transition, and frontal clouds), with the maximum enhancement in ice crystal number concentrations up to 4 orders of magnitude in moderately supercooled clouds. We reveal that SIP is the dominant source of ice crystals near the cloud base for the long-lived Arctic single-layer mixed-phase clouds. The model with SIP improves the occurrence and phase partitioning of the mixed-phase clouds, reverses the vertical distribution pattern of ice number concentrations, and provides a better agreement with observations. The findings of this study highlight the importance of considering SIP in GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Understanding Soil Microbial Sources of Nitrous Acid and their Effect on Carbon-Nitrogen Cycle Interactions

Descriptions of soil emissions of reactive nitrogen (NO y ) in climate models are underdeveloped or non-existent, due to the fact that details of the mechanisms leading to nitrous acid (HONO) and nitrogen di-oxide (NO 2 ) formation in soil are lacking. This represents a major gap in our understanding of a significant land-atmosphere interaction that prevents us from scaling these processes from the laboratory scale to the ecosystem and global scales. There is a critical need to include these mechanisms into climate models since NO y controls the oxidative capacity of the atmosphere and the lifetime of greenhouse gases and the rate of secondary aerosol formation that directly and indirectly affect climate. The first objective of this proposal is to conduct laboratory and field measurements of NO y fluxes from diverse soil types and determine the mechanism of biogenic NO y formation. The working hypothesis based on preliminary data is that soil HONO and NO 2 is ultimately derived from ammonia-oxidizing archaea (AOA) and bacteria (AOB) that are widespread, but whose abundance varies across ecosystems. In the case of NO 2 , reactive oxygen species derived from iron-containing minerals and heterotrophic bacteria drive NO-to-NO 2 conversion. The approach is to link soil fluxes of HONO and NO 2 to AOA, AOB, and other heterotrophs using a combination of laboratory and field experiments, isotopic analysis, and molecular techniques to address how variability in land surfaces and edaphic properties impact emissions. In addition, we will determine the effect of HONO and NO 2 on nitrogen immobilization and the photo-oxidative capacity of soil. The working hypothesis is that HONO uptake in soil will be a source of nitrosonium and hydroxyl radical that will lead to thermal- and photodegradation of soil organic matter to CO 2 and CO, and the incorporation of N in soil organic matter. We will use surface-sensitive mass spectrometry techniques and gas phase detection to study N-immobilization in soil and subsequent enhancements in reactivity that lead to decomposition of organic matter. The results will be used to reduce the uncertainty in projections from the Community Earth System Model (CESM) stemming from inaccurate representations of soil NO y emissions. The proposed research is significant because, in addition to demonstrating new mechanisms of NO y formation and loss, it will be a crucial first step towards modeling the land-atmosphere exchange of HONO and NO 2 in the CESM. Improved model treatment of land-air exchange of NO y is key for understanding feedbacks between human activity and climate, and addressing societal concerns about the fate of N and C emitted to the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Intraseasonal and Interannual Variability of Mars Present Climate

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. The focus of this JRI has been to investigate the nature of intraseasonal and interannual variability of Mars'present climate. We have applied a three-dimensional climate model based on the full hydrostatic primitive equations to determine the spatial, but primarily, the temporal structures of the planet's large-scale circulation as it evolves during a given seasonal advance, and, over multi-annual cycles. The particular climate model applies simplified physical parameterizations and is computationally efficient. It could thus easily be integrated in a perpetual season or advancing season configuration, as well as over many Mars years. We have assessed both high and low-frequency components of the circulation (i.e., motions having periods of Omicron(2-10 days) or greater than Omicron(10 days), respectively). Results from this investigation have explored the basic issue whether Mars' climate system is naturally 'chaotic' associated with nonlinear interactions of the large-scale circulation-regardless of any allowance for year-to-year variations in external forcing mechanisms. Titles of papers presented at scientific conferences and a manuscript to be submitted to the scientific literature are provided. An overview of a areas for further investigation is also presented.

Hollingsworth, Jeffery L.↗