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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Marine Shallow Cloud Adjustments to the Presence of Shortwave-Absorbing Aerosols: Advancing Understanding Through a Combined Analysis of Lasic Datasets and Process Modeling (Final Report)

This is a final report for this award, whose purpose was to support the analysis of data emanating from the LASIC (Layered Atlantic Smoke Interaction with Clouds) campaign. (Layered Atlantic Smoke Interactions with Clouds) is a strategy to improve our understanding of aged carbonaceous aerosol, its seasonal evolution, and the mechanisms by which clouds adjust to the presence of the aerosol. The observational strategy centers on deploying the AMF1 cloud, aerosol, and atmospheric profiling instrumentation to Ascension Island, located within the trade-wind shallow cumulus regime (14.50W, 80S) 3000 km offshore of continental Africa. The location is within the latitude zone of the maximum outflow of aerosol, with the deepening boundary layer known to entrain free-tropospheric smoke. The primary activities for LASIC are: 1) to improve current knowledge on aged biomass burning aerosol and its radiative properties as a function of the seasonal cycle; 2) to use surface-based remote sensing to sensitively interrogate the atmosphere for the relative vertical location of aerosol and clouds; 3) to improve our understanding of the cloud adjustments to the presence of shortwave-absorbing aerosol within the vertical column, both through aerosol-radiation and through aerosol-cloud interactions; 4) to aid low cloud parameterization efforts for climate models. This award serves to further these activities and includes a sub-award to Dr. Pablo Saide at UCLA for activities 3) and 4).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate

A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.

Atmosphere model↗
DOE OSTI2018

Understanding and Improving the Scale Dependence of Trigger Functions for Convective Parameterization Using Cloud-Resolving Model Data

As the resolution of global climate model increases, whether trigger functions in current convective parameterization schemes still work remains unknown. In this study, the scale dependence of undilute and dilute dCAPE, Bechtold, and heated condensation framework (HCF) triggers is evaluated using the cloud-resolving model (CRM) data. It is found that all these trigger functions are scale dependent, especially for dCAPE-type triggers, with skill scores dropping from ~0.6 at the lower resolutions (128, 64, and 32 km) to only ~0.1 at 4 km. The average convection frequency decreases from 14.1% at 128 km to 2.3% at 4 km in the CRM data, but it increases rapidly in the dCAPE-type triggers and is almost unchanged in the Bechtold and HCF triggers across resolutions, all leading to large overpredictions at higher resolutions. In the dCAPE-type triggers, the increased frequency is due to the increased rate of dCAPE greater than the threshold (65 J kg −1 h −1 ) at higher resolutions. The box-and-whisker plots show that the main body of dCAPE in the correct prediction and overprediction can be separated from each other in most resolutions. Moreover, the underprediction is found to be corresponding to the decaying phase of convection. Hence, two modifications are proposed to improve the scale dependence of the undilute dCAPE trigger: 1) increasing the dCAPE threshold and 2) considering convection history, which checks whether there is convection prior to the current time. With these modifications, the skill at 16 km, 8 km, and 4 km can be increased from 0.50, 0.27, and 0.15 to 0.70, 0.61, and 0.53, respectively.

Song, Fengfei↗
DOE OSTI2021

“Scrutinizing entrainment and mass flux closures in shallow cumulus parameterizations using cloud-radar observations and large-eddy simulation” (Final report for DOE grant)

Warm, shallow clouds are responsible for a substantial portion of Earth System Model (ESM) uncertainty and divergence among model climate projections. A substantial amount of this uncertainty is tied to the representation of convection in ESMs, in particular assumptions underlying the convective parameterizations. This study focuses on entrainment and entrainment closures in ESM parameterizations. The completed project was a joint observational and modeling study to observationally derive estimates of entrainment and to use high-resolution modeling to scrutinize entrainment and mass flux closures in shallow convection parameterizations.

58 GEOSCIENCES↗
DOE OSTI2022

Scrutinizing entrainment and mass flux closures in shallow cumulus parameterizations using cloud-radar observations and large-eddy simulation (Final Report)

Representing warm, shallow clouds is a challenge for Global Climate Models, which leads to uncertainty in model projections of future climate. Our study was particularly concerned with how climate models represent the effects of shallow, non-precipitating cumulus clouds. Climate models must make certain assumptions when representing low clouds such as how much dry air from outside the cloud is mixed into the warm, moist cloud updraft. This process is called “entrainment” and tends to weaken the cloud updraft and dry out the cloud, compared to a theoretical cloud with no mixing. This project was a joint observational and modeling study to derive estimates of entrainment from field observations and use high-resolution modeling to evaluate the assumptions used in climate model representations of shallow cumulus. Our research took advantage of a wide variety of Department of Energy measurements from fixed climate-monitoring locations over the Southern Great Plains of the U.S. and the in the Eastern North Atlantic, along with short-term field projects (MAGIC, CAP–MBL, ACE–ENA). During the project, the PI maintained regular collaborations with Department of Energy Scientists at Brookhaven and Argonne National Laboratories. The project funded two students, who both obtained their M.S. in Atmospheric Science and are currently in the Atmospheric Science Ph.D. program. Both were lead authors on research papers funded by this grant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An Improved Algorithm for Estimating Surface Shortwave Radiation: Preliminary Evaluation With MODIS Products

Cloud parameters, as key inputs in radiative transfer algorithms, have a critical impact on surface shortwave radiation (SSR) computation. By introducing a parameterization of cloud transmittance and reflectance, based on radiative transfer simulations, this study improves the accuracy of an existing physically based model which severely underestimates SSR under thick cloud conditions. The cloud parameterization adopts the single-layer cloud model and simulates cloud transmittances and reflectances by varying cloud optical thickness, cloud particle size, and solar zenith angle. The revised model is applied to estimate instantaneous SSR using Moderate-resolution Imaging Spectroradiometer (MODIS) atmospheric and land products. The retrieved SSR is evaluated against observation data from 41 Baseline Surface Radiation Network (BSRN) stations and is also compared with the MODIS official SSR product. The root mean square error (RMSE) of the estimated instantaneous radiation is approximately 52 and 98 W m -2 under clear-sky and all-sky conditions, respectively. The accuracy of the improved parameterization is higher than that of the original model, and there is no obvious underestimation of SSR in the case of high cloud optical thickness. Therefore, the new algorithm improves the accuracy of SSR estimates in the presence of thick clouds. Retrievals with the improved model also achieve higher accuracy than the MODIS official SSR product (MCD18A1). To conclude, the reliable performance of the scheme at most BSRN stations illustrates that the improved model can be used to map SSR on a global scale.

42 ENGINEERING↗
DOE OSTI2022

Evaluating and Improving Scale–Awareness of a Convective Parameterization Closure Using Cloud–Resolving Model Simulations of Convection

This study aims to evaluate a revised closure of the Zhang-McFarlane (ZM) scheme for deep convection and to further improve its scale awareness. Output from two cloud-resolving model (CRM) simulations of both midlatitude organized (MC3E) and tropical unorganized (TWP-ICE) convection are used for the evaluation. By averaging the CRM output over different subdomain sizes from 64 km to 4 km, convection-associated fields are obtained to represent the fields at different horizontal resolutions of global climate models (GCMs). The convection parameterization closure is then tested using these fields. Results show that the cloud base mass flux M b determined from the closure has a relatively good relationship with CRM-simulated convection at coarse resolutions for both MC3E and TWP-ICE, but their correlation degrades at higher resolutions, especially when approaching the gray zone scale under 10 km. The fluctuation of CAPE consumption rate per unit cloud base mass flux for different convective events is found to be an important factor affecting the correlation. When averaged over the base domain, the cloud base mass flux determined by the closure is found to increase with decreasing subdomain size, implying that the closure is not scale-aware. To improve its scale-awareness, a modified closure is designed to alleviate the overprediction of convection at high GCM resolutions. Furthermore, results show that the modified closure has the scale-aware ability to suppress the overprediction problem at high GCM resolutions for both MC3E and TWP-ICE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Constraints on the Parameterization of Convective Cloud Systems from Analyses of ARM Observations and Models

Anthony D. Del Genio has served as principal investigator since the inception of ASR in 2009 as co-chair of the Cloud Lifecycle Working Group (CLWG) and in connection with that as a member of the ASR Science and Infrastructure Steering Committee. In this role he has been intimately involved with oversight of existing and proposed ASR Focus Groups (Vertical Velocity, QUICR, Ice Properties) and thematic interest groups (Mesoscale Organized Convection, Warm Low Clouds, Mixed-Phase Clouds, MJO, Entrainment) whose activities fall under the purview of CLWG. The PI actively participates himself in the Mesoscale Organized Convection and MJO groups, is a Co-Investigator on two successfully implemented IOPs (AMIE, MC3E), and was an unfunded Collaborator on the recently completed FASTER project at BNL under the DOE Modeling Program. The PI has also spent the past three years as a member of the ARM Science Board, which reviews proposals to conduct IOPs at the permanent ARM sites and to deploy the ARM Mobile Facilities. Finally, the PI was a participant in the first ARM-European workshop in 2012 that eventually led to the creation of the SGP supersite concept. This report includes an annotated list of published research results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing Clouds Using Satellite Observations Through Three Generations of Global Atmosphere Models

Abstract Clouds are parameterized in climate models using quantities on the model grid‐scale to approximate the cloud cover and impact on radiation. Because of the complexity of processes involved with clouds, these parameterizations are one of the key challenges in climate modeling. Differences in parameterizations of clouds are among the main contributors to the spread in climate sensitivity across models. In this work, the clouds in three generations of an atmosphere model lineage are evaluated against satellite observations. Satellite simulators are used within the model to provide an appropriate comparison with individual satellite products. In some respects, especially the top‐of‐atmosphere cloud radiative effect, the models show generational improvements. The most recent generation, represented by two distinct branches of development, exhibits some regional regressions in the cloud representation; in particular the southern ocean shows a positive bias in cloud cover. The two branches of model development show how choices during model development, both structural and parametric, lead to different cloud climatologies. Several evaluation strategies are used to quantify the spatial errors in terms of the large‐scale circulation and the cloud structure. The Earth mover's distance is proposed as a useful error metric for the passive satellite data products that provide cloud‐top pressure‐optical depth histograms. The cloud errors identified here may contribute to the high climate sensitivity in the Community Earth System Model, version 2 and in the Energy Exascale Earth System Model, version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

“Evaluation of boundary layer cloud processes in an advanced parameterization for global models” (Final Report)

Low clouds over the ocean are a critical component of our planet’s energy budget because they emit almost as much energy as the surface while being much more reflective than the ocean, resulting in a strong net cooling effect. Despite their importance, low marine clouds remain a persistent source of uncertainty in future climate projections. This project explored how the dynamics, thermodynamics and spatial organization of low clouds is represented in a particular physical simplification, or parameterization, of small-scale air motions and cloud processes commonly employed in global atmospheric models used for short-term weather and longer-term earth system prediction. The overall goal was to develop long-term observational products to constrain numerical model representations of turbulence and cloud processes using observations from the US Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) program’s Eastern North Atlantic (ENA) site. These constraints were then used to demonstrate the type of information that can be gained from confronting simulations with the observations and we assessed the performance of a parameterization in simulating periods of low clouds at ENA. Finally, we used detailed computational fluid dynamics simulations of turbulence and cloud properties to evaluate general physical assumptions embedded in simplified representations of clouds and turbulence for global models of the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Understanding cloud droplet spectral dispersion effect using empirical and semi-analytical parameterizations in NCAR CAM5.3

Five parameterizations of cloud droplet spectral shape are implemented in a global climate model to investigate the dispersion effect and aerosol indirect effect (AIE). We design a series of experiments by modifying the microphysical cloud scheme of NCAR CAM5.3 (Community Atmospheric Model Version 5.3). We employ four empirical (Martin94, RLiu03, PengL03, Liu08) and one semi-analytical (LiuLi15) expressions for cloud droplet spectral shape parameters. Analysis focuses on the instantaneous differences in the simulated cloud microphysical properties and the comparison between model output and satellite data. The results show that RLiu03, PengL03 and LiuLi15 produce wider droplet spectrum and faster autoconversion rate, but Liu08 has a narrower droplet spectrum and slower autoconversion rate than the default parameterization (Martin94) in CAM5.3. Global dispersion effects caused by the five parameterizations modify the aerosol indirect effect by -10% (counteract) to 13% (strengthen). The simulated AIEs and dispersion effects exhibit noticeably spatial inhomogeneity. In the sensitive regions of AIE (Southeast Asia, North Pacific and west coast of South America), we decompose the response of shortwave cloud forcing to the change in droplet number for analysis. The varying dispersion effects can be explained by different responses of cloud properties in different spectral parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Modeling of Aircraft Measurements of Ice Crystal Concentration in the Arctic and a Parameterization for Mixed-Phase Cloud

Abstract In this study, two parameterizations of ice nucleation rate on dust particles are used in a parcel model to simulate aircraft measurements of ice crystal number concentration Ni in the Arctic. The parcel model has detailed microphysics for droplet and ice nucleation, growth, and evaporation with prescribed vertical air velocities. Three dynamic regimes are considered, including large-scale ascent, cloud-top generating cells, and their combination. With observed meteorological conditions and aerosol concentrations, the parcel model predicts the number concentrations of size-resolved ice crystals, which may be compared to aircraft measurements. Model results show rapid changes with height/time in relative humidity, Ni, and thermodynamic phase partitioning, which is not resolved in current climate and weather forecasting models. Parameterizations for ice number and nucleation rate in mixed-phase stratus clouds are thus developed based on the parcel model results to represent the time-integrated effect of some microphysical processes in large-scale models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reconciling Contrasting Relationships Between Relative Dispersion and Volume-Mean Radius of Cloud Droplet Size Distributions

Cloud droplet spectral relative dispersion is critical to parameterizations of cloud radiative properties, warm-rain initiation, and aerosol-cloud interactions in models; however, there is no consistent relationship between relative dispersion and volume-mean radius in literature, which hinders improving relative dispersion parameterization and calls for physical explanation. Here we show, by analyzing aircraft observations of cumulus clouds during Routine AAF [Atmospheric Radiation Measurement (ARM) Aerial Facility] Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations, that the correlation between relative dispersion and volume-mean radius changes from positive to negative as volume-mean radius increases. With the new observation, we postulate that the sign of the correlation is determined by whether or not condensation (evaporation) occurs simultaneously with significant new activation (deactivation). The hypothesis is validated by simulations of both an adiabatic cloud parcel model and a parcel model accounting for entrainment-mixing. A new quantity, first bin strength, is introduced to quantify this new observation. Theoretical analysis of truncated gamma and modified gamma size distributions further supports the hypothesis and reconciles the contrasting relationships between relative dispersion and volume-mean radius, including the results in polluted fog observations. The results could shed new light on the so-called “twilight zone” between cloudy and cloud-free air, which in turn affects evaluation of aerosol-cloud interactions and retrieval of aerosol optical depth.

Lu, Chunsong↗
DOE OSTI2025

An Improved Convection Parameterization with Detailed Aerosol–Cloud Microphysics for a Global Model

Abstract A new microphysical treatment that includes aerosol–cloud interactions and secondary ice production (SIP) mechanisms is implemented in the convection scheme of the Community Atmosphere Model, version 6 (CAM6). The approach is to embed a 1D Lagrangian parcel model in the bulk convective plume of the existing deep convection parameterization. Aerosol activation, growth processes including collision/coalescence, and three processes of SIP mechanisms, two of which are normally overlooked in atmospheric models, are represented in this embedded parcel model. These microphysical processes are treated with a hybrid bin/bulk scheme and a high spatial and temporal resolution for the integration of the embedded parcel in 1D, allowing vertical velocity to determine the microphysical evolution following the in-cloud motion during ascent. Simulations of an observed case (Midlatitude Continental Convective Clouds Experiment) of a mesoscale convective system in Oklahoma, United States, with a single-column model (SCAM) version of CAM, are compared with aircraft in situ and ground-based observations of microphysical properties from the convection and precipitation. Results from the validation show the new microphysical scheme has a good representation of the ice initiation in the bulk convective plume, including the known and empirically quantified pathways of primary and secondary initiation, with benefits for the accuracy of properties of its supercooled cloud liquid. The sensitivity simulations and use of tagging tracers for the validated simulation confirm that the newly included SIP mechanisms are of paramount importance for convective microphysics and can be successfully treated in the global model.

54 ENVIRONMENTAL SCIENCES↗