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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

On the Prediction of Aerosol-Cloud Interactions Within a Data-Driven Framework

Aerosol-cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration N c from aerosol number concentration N a and ambient conditions using a data-driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate N c . We show that the campaign-wide N c can be successfully predicted using machine learning models despite the strongly nonlinear and multi-scale nature of ACI. However, the observation-trained machine learning model fails to predict N c in individual cases while it successfully predicts N c of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data-driven framework, the N c prediction is uncertain at fine spatiotemporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LAACI – Light Absorbing Aerosol-Cloud Interactions Experiment

The “Light Absorbing Aerosol-Cloud Interactions Experiment” project aimed at studying the interactions of light-absorbing aerosols from biomass burning (e.g., wildfires or prescribed fires) with clouds. The core of the project was carried out at the Michigan Technological University turbulent cloud chamber facility (hereafter referred to as the Pi-Chamber). Light-absorbing aerosols from biomass burning include black carbon (BC) and brown carbon (BrC) organic material.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

Buffering of Aerosol-Cloud Adjustments by Coupling Between Radiative Susceptibility and Precipitation Efficiency

Aerosol-cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility.

perturbed parameter ensemble↗
NASA NTRS2018

A13H-2537: Above Cloud Aerosol Optical Properties in the South East Atlantic from airborne observations during ORACLES

Aerosol overlying marine stratocumulus clouds is a persistent feature in the southeast Atlantic during the biomass burning season, July through November. We sampled aerosol and cloud from an airborne research platform, the NASA P3, during the deployments of ObseRvations of CLouds above Aerosols and their intEractionS (ORACLES) encompassing the peak biomass burning season. We used the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) to quantify the aerosol’s optical properties, notably the aerosol optical depth (AOD) by directly measuring the amount of light attenuation. We present an overview of very recent AOD measurements and contrast those to previous deployments. We observed an average AOD at 500 nm of 0.33 (0.30) in September 2016 (August 2017), with similar spatial dependence as observed by the MODIS instrument on board the TERRA and AQUA satellites. We also show full spectral vertical dependence of AOD from flight profiles, which compares favorably to the integrated aerosol extinction profile obtained by the active remote sensing High Spectral Resolution Lidar-2 (HSRL-2). We also present a quantification of the gap extent separating cloud top and aerosol layer bottom. Using hyperspectral transmitted light measurements from 4STAR, in conjunction with hyperspectral hemispheric irradiance measurements from the Solar Spectral Flux Radiometers (SSFR), we retrieved aerosol intensive properties (asymmetry parameter, single scattering albedo), aerosol size distributions, cloud optical depth (COD), cloud particle effective radius, and cloud thermodynamic phase. Cloud properties, also presented here, are obtained from 4STAR measurements of scattered light below clouds.

Samuel E LeBlanc↗
DOE OSTI2025

RCEMIP‐ACI: Aerosol‐Cloud Interactions in a Multimodel Ensemble of Radiative‐Convective Equilibrium Simulations

Aerosol‐cloud interactions are a persistent source of uncertainty in climate research. This study presents findings from a model intercomparison project examining the impact of aerosols on clouds and climate in convection‐permitting radiative‐convective equilibrium (RCE) simulations. Specifically, 11 different modeling teams conducted RCE simulations under varying aerosol concentrations, domain configurations, and sea surface temperatures (SSTs). We analyze the response of domain‐mean cloud and radiative properties to imposed aerosol concentrations across different SSTs. Additionally, we explore the potential impact of aerosols on convective aggregation and large‐scale circulation in large‐domain simulations. The results reveal that the cloud and radiative responses to aerosols vary substantially across models. However, a common trend across models, SSTs, and domain configurations is that increased aerosol loading tends to suppress warm rain formation, enhance cloud water content in the mid‐troposphere, and consequently increase mid‐tropospheric humidity and upper‐tropospheric temperature, thereby impacting static stability. The warming of the upper troposphere can be attributed to reduced lateral entrainment effects due to the higher environmental humidity in the mid‐troposphere. However, models do not agree on aerosol impacts on convective updraft velocity based on the preliminary examination of high‐percentiles of vertical velocity at a single mid‐troposheric layer (500 hPa). In large‐domain simulations, where convection tends to self‐organize, aerosol loading does not consistently influence self‐organization but tends to reduce the intensity of large‐scale circulation forming between convective clusters and dry regions. This reduction in circulation intensity can be explained by the increase in static stability due to the upper tropospheric warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Misrepresentation of the Southern African Easterly Jet in Models and Its Implications for Aerosol, Clouds, and Precipitation Distributions

The southern African easterly jet (AEJ-S) is an important midtropospheric feature critical to understanding the tropical convective system over central Africa and the aerosol–cloud interactions over the southeast Atlantic Ocean. However, it remains unclear how well models represent the AEJ-S and its influence on aerosol transport, clouds, and precipitation distribution. Here, in this work, we use ground- and satellite-based observations and reanalysis datasets to assess the representation of AEJ-S in the Coupled Model Intercomparison Project phase 6 (CMIP6) models between September and October during the peak of midtropospheric winds, aerosol transport, clouds, and precipitation. We find that most CMIP6 models have difficulty accurately simulating the strength, position, and spatial distribution of the AEJ-S. Specifically, the AEJ-S is relatively weaker and at a slightly lower altitude in the ensemble of CMIP6 models than represented by observation and reanalysis datasets. To assess the influence of the misrepresented the AEJ-S on CMIP6-simulated aerosol, clouds, and precipitation distributions, we performed composite analyses using models with low and high biases based on the estimates of their midtropospheric easterly wind speed. We find that the misrepresentation of the AEJ-S in CMIP6 models is associated with the overestimation of clouds and precipitation over central Africa, the underestimation of clouds over the southeast Atlantic Ocean, and the limitation of aerosol transport over the continent or the deviation of its spatial distribution from the typical zonal transport over the Atlantic Ocean. Because aerosols, clouds, and precipitation are important components of the regional climate system, we conclude that accurate representation of the AEJ-S is essential over central Africa and the southeast Atlantic Ocean.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Multistatic Aerosol Cloud Lidar in Space: A Theoretical Perspective

Accurate aerosol and cloud retrievals from space remain quite challenging and typically involve solving a severely ill-posed inverse scattering problem. In this Perspective, we formulate in general terms an aerosol and aerosol-cloud interaction space mission concept intended to provide detailed horizontal and vertical profiles of aerosol physical characteristics as well as identify mutually induced changes in the properties of aerosols and clouds. We argue that a natural and feasible way of addressing the ill-posedness of the inverse scattering problem while having an exquisite vertical-profiling capability is to fly a multistatic (including bistatic) lidar system. We analyze theoretically the capabilities of a formation-flying constellation of a primary satellite equipped with a conventional monostatic (backscattering) lidar and one or more additional platforms each hosting a receiver of the scattered laser light. If successfully implemented, this concept would combine the measurement capabilities of a passive multi-angle multi-spectral polarimeter with the vertical profiling capability of a lidar; address the ill-posedness of the inverse problem caused by the highly limited information content of monostatic lidar measurements; address the ill-posedness of the inverse problem caused by vertical integration and surface reflection in passive photopolarimetric measurements; relax polarization accuracy requirements; eliminate the need for exquisite radiative-transfer modeling of the atmosphere-surface system in data analyses; yield the day-and-night observation capability; provide direct characterization of ground-level aerosols as atmospheric pollutants; and yield direct measurements of polarized bidirectional surface reflectance. We demonstrate, in particular, that supplementing the conventional backscattering lidar with just one additional receiver flown in formation at a scattering angle close to 170deg can dramatically increase the information content of the measurements. Although the specific subject of this Perspective is the multistatic lidar concept, all our conclusions equally apply to a multistatic radar system intended to study from space the global distribution of cloud and precipitation characteristics.

aerosols↗
NASA NTRS2004

Evidence for Natural Variability in Marine Stratocumulus Cloud Properties Due to Cloud-Aerosol

In this study, aircraft observations from the Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter are used to characterize the variability in drizzle, cloud, and aerosol properties associated with cloud rifts and the surrounding solid clouds observed off the coast of California. A flight made on 16 July 1999 provided measurements directly across an interface between solid and rift cloud conditions. Aircraft instrumentation allowed for measurements of aerosol, cloud droplet, and drizzle spectra. CCN concentrations were measured in addition to standard thermodynamic variables and the winds. A Forward Scatter Spectrometer Probe (FSSP) measured size distribution of cloud-sized droplets. A Cloud Imaging Probe (CIP) was used to measure distributions of drizzle-sized droplets. Aerosol distributions were obtained from a Cloud Aerosol Scatterprobe (CAS). The CAS probe measured aerosols, cloud droplets and drizzle-sized drops; for this study. The CAS probe was used to measure aerosols in the size range of 0.5 micron - 1 micron. Smaller aerosols were characterized using an Ultrafine Condensation Particle Counter (CPC) sensor. The CPC was used to measure particles with diameters greater than 0.003 micron. By subtracting different count concentrations measured with the CPC, this probe was capable of identifying ultrafine particles those falling in the size range of 3 nanometers - 7 nanometers that are believed to be associated with new particle production.

Albrecht, Bruce↗
NASA NTRS2014

The Airborne Cloud-Aerosol Transport System: Overview and Description of the Instrument and Retrival Algorithms - Part I

The Airborne Cloud-Aerosol Transport System (ACATS) is a multi-channel Doppler lidar system recently developed at NASA Goddard Space Flight Center (GSFC). A unique aspect of the multi-channel Doppler lidar concept such as ACATS is that it is also, by its very nature, a high spectral resolution lidar (HSRL). Both the particulate and molecular scattered signal can be directly and unambiguously measured, allowing for direct retrievals of particulate extinction. ACATS is therefore capable of simultaneously resolving the backscatterextinction properties and motion of a particle from a high altitude aircraft. ACATS has flown on the NASA ER-2 during test flights over California in June 2012 and science flights during the Wallops Airborne Vegetation Experiment (WAVE) in September 2012. This paper provides an overview of the ACATS method and instrument design, describes the ACATS retrieval algorithms for cloud and aerosol properties, and demonstrates the data products that will be derived from the ACATS data using initial results from the WAVE project. The HSRL retrieval algorithms developed for ACATS have direct application to future spaceborne missions such as the Cloud-Aerosol Transport System (CATS) to be installed on the International Space Station (ISS). Furthermore, the direct extinction and particle wind velocity retrieved from the ACATS data can be used for science applications such 27 as dust or smoke transport and convective outflow in anvil cirrus clouds.

HSRL↗
DOE OSTI2025

Regime-based aerosol–cloud interactions from CALIPSO-MODIS and the Energy Exascale Earth System Model version 2 (E3SMv2) over the Eastern North Atlantic

This study investigates aerosol-cloud interactions in marine boundary layer (MBL) clouds using an advanced deep-learning-driven synoptic-regime-based framework, combining satellite data (CALIPSO vertically resolved aerosol extinction and MODIS cloud properties) with 1° nudged Energy Exascale Earth System Model version 2 (E3SMv2) simulation over the Eastern North Atlantic (ENA; ∼10°×10°, 2006–2014). The E3SMv2 captures observed seasonal variations in cloud droplet number concentrations (N d ) and liquid water path (LWP), though it systematically underestimates N d . We then partition ENA meteorology into four synoptic regimes (Pre-Trough, Post-Trough, Ridge, Trough) via a deep-learning clustering of ERA5 reanalysis fields, enabling regime-dependent aerosol-cloud interactions analyses. Both satellite and E3SMv2 exhibit an inverted-V LWP-N d relationship. In Post-Trough and Ridge regimes, the satellite shows stronger negative LWP-N d sensitivities than in Pre-Trough regime. The Trough regime displays a muted satellite LWP response. In comparison, the model predicts more exaggerated LWP responses across regimes, with LWP increasing too quickly at low N d and decreasing more sharply at high N d , especially in Pre-Trough and Trough regimes. These exaggerated model LWP sensitivities may stem from uncertainties in representing drizzle processes, entrainment, and turbulent mixing. As for N d susceptibility to aerosols, N d increases with MBL aerosol extinction in both datasets, but the simulated aerosol-cloud interactions appear oversensitive to meteorological conditions. Overall, E3SMv2 better captures aerosol effects under regimes that favor stratiform clouds (Post-Trough, Ridge), but performance deteriorates for regimes with deeper, dynamically complex clouds (Trough), highlighting the need for improved representations of those cloud processes in climate models.

Environmental sciences↗
DOE OSTI2020

Quantification of the Radiative Effect of Aerosol–Cloud Interactions in Shallow Continental Cumulus Clouds

The indirect radiative effect of aerosol variability on shallow cumulus clouds is realized in nature with considerable concurrent meteorological variability. Large-eddy simulations constrained by observations at a continental site in Oklahoma are performed to represent the variability of different meteorological states on days with different aerosol conditions. The total radiative effect of this natural covariation between aerosol and other meteorological drivers of total cloud amount and albedo is quantified. The changes to these bulk quantities are used to understand the response of the cloud radiative effect to aerosol–cloud interactions (ACI) in the context of concurrent processes, as opposed to attempting to untangle the effect of individual processes on a case-by-case basis. Mutual information (MI) analysis suggests that meteorological variability masks the strength of the relationship between cloud drop number concentration and the cloud radiative effect. This is shown to be mostly due to variation in solar zenith angle and cloud field horizontal heterogeneity masking the relationship between cloud drop number and cloud albedo. By combining MI and more traditional differential analyses, a framework to identify important modes of covariation between aerosol, clouds, and meteorological conditions is developed. This shows that accounting for solar zenith angle variation and implementing an albedo bias correction increases the detectability of the radiative effects of ACI in simulations of shallow cumulus.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

A State-of-the-Art Experimental Laboratory for Cloud and Cloud-Aerosol Interaction Research

The state of the art for predicting climate changes due to increasing greenhouse gasses in the atmosphere with high accuracy is problematic. Confidence intervals on current long-term predictions (on the order of 100 years) are so large that the ability to make informed decisions with regard to optimum strategies for mitigating both the causes of climate change and its effects is in doubt. There is ample evidence in the literature that large sources of uncertainty in current climate models are various aerosol effects. One approach to furthering discovery as well as modeling, and verification and validation (V&V) for cloud-aerosol interactions is use of a large "cloud chamber" in a complimentary role to in-situ and remote sensing measurement approaches. Reproducing all of the complex interactions is not feasible, but it is suggested that the physics of certain key processes can be established in a laboratory setting so that relevant fluid-dynamic and cloud-aerosol phenomena can be experimentally simulated and studied in a controlled environment. This report presents a high-level argument for significantly improved laboratory capability, and is meant to serve as a starting point for stimulating discussion within the climate science and other interested communities.

Fremaux, Charles M.↗
DOE OSTI2021

Influence of biogenic emissions from boreal forests on aerosol–cloud interactions

Boreal forest acts as a carbon sink and contributes to the formation of secondary organic aerosols via emission of aerosol precursor compounds. However, these influences on the climate system are poorly quantified. Here we show direct observational evidence that aerosol emissions from the boreal forest biosphere influence warm cloud microphysics and cloud-aerosol interactions in a scale-dependent and highly dynamic manner. Analyses of in situ and ground-based remote-sensing observations from the SMEAR II station in Finland, conducted over eight months in 2014, reveal substantial increases in aerosol load over the forest one to three days after aerosol-poor marine air enters the forest environment. We find that these changes are consistent with secondary organic aerosol formation and, together with water-vapour emissions from evapotranspiration, are associated with changes in the radiative properties of warm, low-level clouds. Here, the feedbacks between boreal forest emissions and aerosol-cloud interactions and the highly dynamic nature of these interactions in air transported over the forest over timescales of several days suggest boreal forests have the potential to mitigate climate change on a continental scale. Our findings suggest that even small changes in aerosol precursor emissions, whether due to changing climatic or anthropogenic factors, may substantially modify the radiative properties of clouds in moderately polluted environments. Emissions from the boreal forest biosphere can substantially increase aerosol load above the forest and influence the radiative properties of clouds, according to analysis of observations from a monitoring station in Finland.

54 ENVIRONMENTAL SCIENCES↗