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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Shortwave absorption by wildfire smoke dominated by dark brown carbon

Abstract Wildfires emit large amounts of black carbon and light-absorbing organic carbon, known as brown carbon, into the atmosphere. These particles perturb Earth’s radiation budget through absorption of incoming shortwave radiation. It is generally thought that brown carbon loses its absorptivity after emission in the atmosphere due to sunlight-driven photochemical bleaching. Consequently, the atmospheric warming effect exerted by brown carbon remains highly variable and poorly represented in climate models compared with that of the relatively nonreactive black carbon. Given that wildfires are predicted to increase globally in the coming decades, it is increasingly important to quantify these radiative impacts. Here we present measurements of ensemble-scale and particle-scale shortwave absorption in smoke plumes from wildfires in the western United States. We find that a type of dark brown carbon contributes three-quarters of the short visible light absorption and half of the long visible light absorption. This strongly absorbing organic aerosol species is water insoluble, resists daytime photobleaching and increases in absorptivity with night-time atmospheric processing. Our findings suggest that parameterizations of brown carbon in climate models need to be revised to improve the estimation of smoke aerosol radiative forcing and associated warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Southern Ocean Aerosols Field Campaign Report

Atmospheric processes over the Southern Ocean have a profound influence on regional and global climate. This is a part of the world where global climate models perform particularly poorly, with models persistently overpredicting the amount of sunlight reaching the Earth's surface (Regayre et al. 2020). A major challenge when trying to improve the representation of atmospheric processes in this part of the globe is the scarcity of observations capable of constraining our understanding. During 2024/25 a U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility field campaign (CAPE-k) deployed a suite of instrumentation at the kennaook/Cape Grim atmospheric monitoring station in Tasmania, Australia to provide detailed cloud-aerosol observations in this region in order to address the greatest source of uncertainty in climate models – atmospheric aerosols and how they influence cloud formation (Carslaw et al. 2013). In support of the ARM observations, an Australian Research Council (ARC)-funded project “Southern Ocean aerosols: sources, sinks and impact on cloud properties,” led by the Queensland University of Technology, complemented the ARM measurements with a suite of chemical measurements at the kennaook/Cape Grim site in northwestern Tasmania to provide more information on the processes controlling aerosol formation and growth. The deployment of the University of York LIF-SO2 instrument (Temple et al. 2025) in the ARM mobile facility container was part of this chemistry-focused deployment. Increased observational efforts are critical for understanding sources and sinks of Southern Ocean aerosols, in particular the role of marine micro-organisms (phytoplankton, algae) on aerosols formation, their properties and growth, cloud droplet formation, and the conversion of cloud droplets to ice crystals and precipitation. The observational focus of the ARC-funded project is aerosol chemical composition and their gaseous precursors. The remote nature of the Southern Ocean poses a significant analytical challenge when studying gaseous precursors, as low concentrations are often below the limits of detection of commercial instrumentation. Historically this has meant that observations have had to be time-averaged over days to weeks to achieve the limits of detection required. Although useful when considering overall average levels of aerosol precursors, this time-averaging obscures temporal variability that can provide insight into the controlling processes. Over recent years advances in analytical techniques have made high-time-resolution measurements of key gas-phase aerosol precursors achievable with sufficiently low limits of detection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Deciphering Atmospheric Ice Nucleation using Molecular-Scale Microscopy

Atmospheric ice affects Earth's radiative properties and initiates most precipitation. Growing ice typically requires a particle, often airborne mineral dust, e.g., to catalyze freezing of supercooled cloud droplets. How chemistry, structure and morphology determine the ice-nucleating ability of minerals remains elusive. Not surprisingly, poor understanding of a erosol-cloud interactions is a major source of uncertainty in climate models. In this project, we combine d optical microscopy with atomic force microscopy to explore the mechanisms of initial ice formation on alkali feldspar, a mineral proposed to dominate ice nucleation in Earth's atmosphere. When cold air becomes supersaturated with respect to water, we discovered that supercooled liquid water condenses at steps without having to overcome a nucleation barrier, and subsequently freezes quickly. Our results imply that steps, common even on macroscopically flat feldspar surfaces, can accelerate water condensation followed by freezing, thus promoting glaciation and dehydration of mixed - phase clouds. Motivated by the fact that current climate simulations do not properly account for feldspar's extreme efficiency to nucleate ice, we modified DOE's climate model, the Energy Exascale Earth System Model (E3SM), to increase the activation of ice nucleation on feldspar dust. This included adding a new aerosol tracer into the model and updating the ice nucleation parameterization, based on Classical Nucleation Theory, for multiple mineral dust tracers. Although t he se modifications have little impact on global averages , predictions of regional averages can be strongly affected .

Thurmer, Konrad↗
DOE OSTI2024

Soil Moisture‐Cloud‐Precipitation Feedback in the Lower Atmosphere From Functional Decomposition of Satellite Observations

Abstract The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Accounting for adaptation and intensity in projecting heat wave-related mortality

Highlights: • We propose novel models for intensity of and adaptation to heat waves in projections of heat wave- mortality. • The modeling used a large national dataset and the projections used a rich set of climate models and scenarios. • Future heat wave-related mortality would be substantially overestimated, if adaptation were ignored. • The time trend of future heat wave-related mortality will vary substantially across climate regions. How adaptation and intensity of heat waves affect heat wave-related mortality is unclear, making health projections difficult.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Attribution of Anthropogenic Influence on Atmospheric Patterns Conducive to Recent Most Severe Haze Over Eastern China

Severe haze pollution in eastern China has caused substantial health impacts and economic loss. Conducive atmospheric conditions are important to affect occurrence of severe haze events, and circulation changes induced by future global climate warming are projected to increase the frequency of such events. However, a potential contribution of an anthropogenic influence to recent most severe haze (December 2015 and January 2013) over eastern China remains unclear. Here we show that the anthropogenic influence, which is estimated by using large ensemble runs with a climate model forced with and without anthropogenic forcings, has already increased the probability of the atmospheric patterns conducive to severe haze by at least 45% in January 2013 and 27% in December 2015, respectively. We further confirm that simulated atmospheric circulation pattern changes induced by anthropogenic influence are driven mainly by increased greenhouse gas emissions. Our results suggest that more strict reductions in pollutant emissions are needed under future anthropogenic warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Fate of the Atlantic Meridional Overturning Circulation: Strong decline under continued warming and Greenland melting

The most recent Intergovernmental Panel on Climate Change assessment report concludes thatthe Atlantic Meridional Overturning Circulation (AMOC) could weaken substantially but is very unlikely tocollapse in the 21st century. However, the assessment largely neglected Greenland Ice Sheet (GrIS)mass loss, lacked a comprehensive uncertainty analysis, and was limited to the 21st century. Here in acommunity effort, improved estimates of GrIS mass loss are included in multicentennial projections usingeight state-of-the-science climate models, and an AMOC emulator is used to provide a probabilisticuncertainty assessment. We find that GrIS melting affects AMOC projections, even though it is of secondaryimportance. By years 2090–2100, the AMOC weakens by 18% [3%, 34%; 90% probability] in an intermediategreenhouse-gas mitigation scenario and by 37% [15%, 65%] under continued high emissions. Afterward, itstabilizes in the former but continues to decline in the latter to 74% [+4%, 100%] by 2290–2300, with a 44%likelihood of an AMOC collapse. This result suggests that an AMOC collapse can be avoided by CO2mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Projecting future climate change impacts on heat-related mortality in large urban areas in China

Highlights: • The study projects future heat-related mortality risk in 51 major cities in China. • The projections are based on high resolution climate and population projections. • Future heat mortality risk is likely to be considerable under all emission scenarios. • Climate mitigation and heat risk management are needed to reduce such risk. Global climate change is anticipated to raise overall temperatures and has the potential to increase future mortality attributable to heat. Urban areas are particularly vulnerable to heat because of high concentrations of susceptible people. As the world's largest developing country, China has experienced noticeable changes in climate, partially evidenced by frequent occurrence of extreme heat in urban areas, which could expose millions of residents to summer heat stress that may result in increased health risk, including mortality. While there is a growing literature on future impacts of extreme temperatures on public health, projecting changes in future health outcomes associated with climate warming remains challenging and underexplored, particularly in developing countries. This is an exploratory study aimed at projecting future heat-related mortality risk in major urban areas in China. We focus on the 51 largest Chinese cities that include about one third of the total population in China, and project the potential changes in heat-related mortality based on 19 different global-scale climate models and three Representative Concentration Pathways (RCPs). City-specific risk estimates for high temperature and all-cause mortality were used to estimate annual heat-related mortality over two future twenty-year time periods. We estimated that for the 20-year period in Mid-21st century (2041–2060) relative to 1970–2000, incidence of excess heat-related mortality in the 51 cities to be approximately 37,800 (95% CI: 31,300–43,500), 31,700 (95% CI: 26,200–36,600) and 25,800 (95% CI: 21,300–29,800) deaths per year under RCP8.5, RCP4.5 and RCP2.6, respectively. Slowing climate change through the most stringent emission control scenario RCP2.6, relative to RCP8.5, was estimated to avoid 12,900 (95% CI: 10,800–14,800) deaths per year in the 51 cities in the 2050s, and 35,100 (95% CI: 29,200–40,100) deaths per year in the 2070s. The highest mortality risk is primarily in cities located in the North, East and Central regions of China. Population adaptation to heat is likely to reduce excess heat mortality, but the extent of adaptation is still unclear. Future heat mortality risk attributable to exposure to elevated warm season temperature is likely to be considerable in China's urban centers, with substantial geographic variations. Climate mitigation and heat risk management are needed to reduce such risk and produce substantial public health benefits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗
DOE OSTI2019

Cloud, Aerosol and Boundary Layer Structure across the Northeast Pacific Stratocumulus-Cumulus Transition as observed during CSET

During the Cloud System Evolution in the Trades (CSET) field study, 14 research flights of the National Science Foundation G-V sampled the stratocumulus-cumulus transition between Northern California and Hawaii and its synoptic variability. The G-V made vertically resolved measurements of turbulence, cloud microphysics, aerosol characteristics, and trace gases. It also carried dropsondes and a vertically pointing W-band radar and lidar. This paper summarizes these observations with the goals of fostering novel comparisons with theory, models and reanalyses, and satellite-derived products. A longitude-height binning and compositing strategy mitigates limitations of sparse sampling and spatiotemporal variability. Typically, a 1-km-deep decoupled stratocumulus-capped boundary layer near California evolved into 2-km-deep precipitating cumulus clusters surrounded by patches of thin stratus that dissipated toward Hawaii. Low cloud cover was correlated with estimated inversion strength more than with cloud droplet number, even though the thickest clouds were generally precipitating and ultraclean layers indicative of aerosol-cloud-precipitation interaction were common west of 140 degrees W. Accumulation-mode aerosol concentration correlated well with collocated cloud droplet number concentration and was typically largest near the surface. Aitken mode aerosol concentration was typically larger in the free troposphere. Wildfire smoke produced spikes of aerosol and trace gases on some flights. CSET data are compared with space-time collocated output from MERRA-2 reanalysis and from the CAM6 climate model run with winds and temperature nudged toward this reanalysis. The reanalysis compares better with the observed relative humidity than does nudged CAM6. Both vertically diffuse the stratocumulus cloud layer versus observations. MERRA-2 slightly underestimates in situ carbon monoxide measurements and underestimates ozone depletion within the boundary layer.

Bretherton, Christopher S.↗
DOE OSTI2018

Local radiative feedbacks over the Arctic based on observed short-term climate variations

We compare various radiative feedbacks in response to short-term climate variations estimated from reanalysis, satellite, and global climate model datasets using the combined Kernel-Gregory approach over the Arctic (60-90°N). Except for the shortwave cloud feedback, all feedbacks are positive over the Arctic. Lapse rate feedback is comparable with surface albedo feedback. Relative to the tropics (30°S-30°N), the temperature feedback (lapse rate feedback plus Planck feedback deviation from its global mean) is the largest contributor to Arctic amplification among all feedbacks, followed by surface albedo feedback. The choice of meteorological fields has a small impact on the cloud feedback estimate, but different top-of-the-atmosphere clear-sky fluxes datasets can result in a wide spread of estimated cloud feedbacks. The net cloud feedback over the Arctic is positive due to a larger positive in longwave than the negative shortwave feedback, but has the largest uncertainty. Relative to the tropics, the longwave cloud feedback largely contributes to the Arctic amplification.

Zhang, Rudong↗
DOE OSTI2019

Return period of extreme rainfall substantially decreases under 1.5 °C and 2.0 °C warming: a case study for Uttarakhand, India

In June 2013, Uttarakhand experienced a hydro-meteorological disaster due to a 4 d extreme precipitation event of return period more than 100 years, claiming thousands of lives and causing enormous damage to infrastructure. Using the weather@home climate modelling system and its Half a degree Additional warming, Prognosis and Projected Impacts simulations, this study investigates the change in the return period of similar events in a 1.5 °C and 2 °C warmer world, compared to current and pre-industrial levels. We find that the likelihood of such extreme precipitation events will significantly increase under both future scenarios. We also estimate the change in extreme river flow at the Ganges; finding a considerable increase in the risk of flood events. Our results also suggest that until now, anthropogenic aerosols may have effectively counterbalanced the otherwise increased meteorological flood risk due to greenhouse gas (GHG) induced warming. Disentangling the response due to GHGs and aerosols is required to analyses the changes in future rainfall in the South Asia monsoon region. More research with other climate models is also necessary to make sure these results are robust.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Fitting Formulae and Constraints for the Existence of S-type and P-type Habitable Zones in Binary Systems

We derive fitting formulae for the quick determination of the existence of S-type and P-type habitable zones (HZs) in binary systems. Based on previous work, we consider the limits of the climatological HZ in binary systems (which sensitively depend on the system parameters) based on a joint constraint encompassing planetary orbital stability and a habitable region for a possible system planet. Additionally, we employ updated results on planetary climate models obtained by Kopparapu and collaborators. Our results are applied to four P-type systems (Kepler-34, Kepler-35, Kepler-413, and Kepler-1647) and two S-type systems (TrES-2 and KOI-1257). Our method allows us to gauge the existence of climatological HZs for these systems in a straightforward manner with detailed consideration of the observational uncertainties. Further applications may include studies of other existing systems as well as systems to be identified through future observational campaigns.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Revisiting the Role of Ocean Circulation Changes in Polar Ocean Heat Transport Anomalies under Global Warming

In response to greenhouse gas forcing, climate models predict that poleward ocean heat transport (OHT) weakens in the Southern Ocean but increases in the Arctic. The role of ocean circulation changes in this OHT response has been evaluated by decomposing OHT anomalies into a dynamic component (holding ocean temperature fixed while circulation evolves) and a thermodynamic component (holding ocean circulation fixed while temperature evolves). However, ocean temperature changes are themselves shaped by circulation changes through redistribution of the existing heat reservoir and subsequent effects on air–sea heat fluxes. The thermodynamic component can therefore be influenced by circulation changes, making the standard thermodynamic–dynamic decomposition incomplete for isolating the role of circulation changes in OHT anomalies. To address this issue, we use a passive–active decomposition to assess the relative contributions of ocean circulation and passive ocean temperature changes to polar OHT anomalies in a fully coupled climate model. Passive temperature changes are defined as those thermally forced by the atmosphere in the absence of circulation changes. In this passive–active decomposition, an advective term involving both circulation and passive temperature changes remains ambiguous—classifying it as active implies circulation changes dominate Southern Ocean OHT anomalies, whereas classifying it as passive implies temperature changes dominate. However, both interpretations imply that ocean circulation changes have a much weaker effect on polar OHT anomalies than inferred from the standard decomposition. In conclusion, these results help reconcile conclusions from studies using the standard decomposition with those using passive tracer methods to assess the role of circulation changes in polar OHT anomalies.

Arctic↗
DOE OSTI2019

Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes

Abstract Subgrid-scale (SGS) velocity variations result in gridscale sea surface flux enhancements that must be parameterized in weather and climate models. Traditional parameterizations are deterministic in that they assign a unique value of the SGS velocity flux enhancement to any given configuration of the resolved state. In this study, we assess the statistics of SGS velocity flux enhancement over a range of averaging scales (as a proxy for varying model resolution) through systematic coarse-graining of a convection-permitting atmospheric model simulation over the Indian Ocean and west Pacific warm pool. Conditioning the statistics of the SGS velocity flux enhancement on 1) the fluxes associated with the resolved winds and 2) the precipitation rate, we find that the lack of a separation between “resolved” and “unresolved” scales results in a distribution of flux enhancements for each configuration of the resolved state. That is, the SGS velocity flux enhancement should be represented stochastically rather than deterministically. The spatial and temporal statistics of the SGS velocity flux enhancement are investigated by using basic descriptive statistics and through a fit to an anisotropic space–time covariance structure. Potential spatial inhomogeneities of the statistics of the SGS velocity flux enhancement are investigated through regional analysis, although because of the relatively short duration of the simulation (9 days) distinguishing true inhomogeneity from sampling variability is difficult. Perspectives for the implementation of such a stochastic parameterization in weather and climate models are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Estimates of Decadal Climate Predictability From an Interactive Ensemble Model

Decadal climate predictability has received considerable scientific interest in recent years, yet the limits and mechanisms for decadal predictability are currently not well known. It is widely accepted that noise due to internal atmospheric dynamics at the air-sea interface influences predictability. The purpose of this paper is to use the interactive ensemble (IE) coupling strategy to quantify how internal atmospheric noise at the air-sea interface impacts decadal predictability. The IE technique can significantly reduce internal atmospheric noise and has proven useful in assessing seasonal-to-interannual variability and predictability. Here we focus on decadal timescales and apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. This is the first time the nonlinear local Lyapunov exponent has been applied to the state-of-the-art coupled models. The global patterns of decadal predictability are discussed from the perspective of internal atmospheric noise and ocean dynamics.

58 GEOSCIENCES↗
DOE OSTI2021

Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM

Abstract Mesoscale convective systems (MCSs) are one of the most climatically significant forms of convection because of their large role in water and energy cycles. The mesoscale features associated with MCS are difficult to represent in climate models because the relevant dynamics and physics are absent or poorly represented with coarse model resolution (∼100 km). Using a regionally refined model (RRM) with 0.25° grid spacing embedded in the Energy Exascale Earth System Model (E3SM), we explore the impact of cloud microphysics parameterizations on the simulation of precipitation, particularly MCS precipitation over the contiguous United States. The Predicted Particle Properties (P3) cloud microphysics scheme has been modified and implemented into E3SM to overcome the limitations of the default Morrison and Gettelman (MG2) scheme in which rimed precipitating ice particles (graupel/hail) are absent and frozen particles are artificially partitioned into cloud ice and snow. We show that P3 improves the simulation of precipitation statistics including frequency distribution compared with MG2 with a limited effect on the diurnal cycle. P3 predicts higher hourly rain rates, resulting in 20% more MCSs and a higher total MCS precipitation (4.4%) compared to MG2, agreeing better with observations. The improvements with P3 mainly result from improved representations of ice microphysics, which not only produces higher rain rates through melting but also leads to a stronger large‐scale ascending motion by releasing more latent heating. This study suggests that improving microphysics parameterization is important for simulating MCS precipitation as future climate model resolutions continue to increase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterizing the contribution of bioaerosols diversity from complex aerosol particle samples.

Current global change scenarios expect to significantly increase the contribution of pollen to the total organic aerosol budget. In addition, pollen can burst into smaller fragments that are highly efficient as cloud condensation nuclei. However, current climate models still do not incorporate the effects of these particles due to detection and quantification challenges in complex aerosol mixtures. Chemical biomarkers (i.e. fructose) have commonly been used to trace pollen but this approximation can generate false positive results given the large number of shared compounds between different bioaerosols. A previous pilot EMSL experiment performed on pure pollen samples suggested that using a set of metabolites as a fingerprint of bioaerosols could serve to substantially increase detection accuracy over single biomarker approaches. In this proposal we developed novel bioinformatic strategies to characterize and quantify complex pollen mixtures present in the atmosphere. For that, we used diverse machine learning algorithms to analyze metabolic fingerprints from complex mixtures of different pollen acquired with high-resolution mass spectrometry (Orbitrap Q-Exactive). Our preliminary results demonstrated the potential to accurately discern and identify at specific relative abundances different pollen species in complex mixtures. Our research will lead to a significant advancement in the atmospheric chemistry and provide much needed data to improve the accuracy of climate models.

54 ENVIRONMENTAL SCIENCES↗